bacpipe.embedding_evaluation.visualization.visualize_embeddings

Functions

collect_dim_reduced_embeds(model_name, ...)	Return the dimensionality reduced embeddings of a model.
darken_hex_color_bitwise(hex_color)	Darkens a hex color using the bitwise operation: (color & 0xfefefe) >> 1.
data_split_by_labels(embeds_dict, labels)	Split data by labels for scatterplots.
get_labels_for_plot([model_name])
init_embed_figure(fig, axes[, bool_3d])
plot_comparison(plot_path, models, ...[, ...])	Create big overview visualization of all embeddings spaces.
plot_embedding_points(axes, embeds, ...[, ...])	Plot embeddings in scatter plot.
plot_embeddings(loader, model_name, label_by)	Generate figures and axes to plot points corresponding to embeddings.
plot_embeddings_px(embeds, labels, c_label_dict)
reorder_embeddings_by_clustering_performance(...)	Reorder the embedding overview plot by clustering performance.
return_rows_cols(num)
set_colorbar_or_legend(fig, axes, points, ...)
set_figsize_for_comparison(rows, cols)
set_legend(handles, labels, fig, axes[, ...])	Create the legend for embeddings visualization plots.

Classes

EmbedAndLabelLoader(dim_reduction_model[, ...])
Figure([figsize, dpi, facecolor, edgecolor, ...])	The top level container for all the plot elements.
Path(*args, **kwargs)	PurePath subclass that can make system calls.

class bacpipe.embedding_evaluation.visualization.visualize_embeddings.EmbedAndLabelLoader(dim_reduction_model, dashboard=False, **kwargs)

Bases: object

get_data(model_name, label_by, remove_noise=False, **kwargs)

remove_noise_indices(model_name)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.collect_dim_reduced_embeds(model_name, dim_reduced_embed_path, dim_reduction_model, **kwargs)

Return the dimensionality reduced embeddings of a model.

Parameters:

• model_name (str) – name of model

• dim_reduced_embed_path (pathlib.Path object) – path to dim reduced embeddings

• dim_reduction_model (str) – name of feature extraction model

Returns:

dimensionality reduced embeddings

Return type:

dict

bacpipe.embedding_evaluation.visualization.visualize_embeddings.darken_hex_color_bitwise(hex_color)

Darkens a hex color using the bitwise operation: (color & 0xfefefe) >> 1.

Parameters:

hex_color (str): The hex color string (e.g., ‘#1f77b4’).

Returns:

str: The darkened hex color.

bacpipe.embedding_evaluation.visualization.visualize_embeddings.data_split_by_labels(embeds_dict, labels)

Split data by labels for scatterplots.

Parameters:

• embeds_dict (dict) – embeddings by model

• labels (list) – list of labels

Returns:

x and y data corresponding to labels

Return type:

dict

bacpipe.embedding_evaluation.visualization.visualize_embeddings.get_labels_for_plot(model_name=None, **kwargs)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.init_embed_figure(fig, axes, bool_3d=False, **kwargs)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.plot_comparison(plot_path, models, dim_reduction_model, bool_spherical=False, dashboard=False, loader=None, evaluation_task=[], **kwargs)

Create big overview visualization of all embeddings spaces. Labels are chosen from ground_truth and if that does not exist, default lables are used.

Parameters:

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

• models (list) – list of models

• dim_reduction_model (str) – name of dimensionality reduction model

• bool_spherical (bool, optional) – if True 3d embeddings will be plotted, by default False

• dashboard (bool, optional) – if dashboard called this function or not

• loader (EmbedAndLabelLoader object) – object containing embeds and labels by model for quicker loading

• evaluation_task (list, optional) – list of tasks to evaluate, by default []

Returns:

figure handle

Return type:

plt object

bacpipe.embedding_evaluation.visualization.visualize_embeddings.plot_embedding_points(axes, embeds, split_data, labels, c_label_dict, remove_noise=False, **kwargs)

Plot embeddings in scatter plot.

Parameters:

• axes (plt object) – axes handle

• embeds (dict) – embeddings

• split_data (dict) – data split by label

• labels (list) – labels of the data

• c_label_dict (dict) – linking labels to ints for coloring

• remove_noise (bool, optional) – remove noise or not, defaults to False

Returns:

axes points

Return type:

plt object

bacpipe.embedding_evaluation.visualization.visualize_embeddings.plot_embeddings(loader, model_name, label_by, paths=None, dim_reduction_model=None, axes=False, fig=False, dashboard=False, dashboard_idx=None, **kwargs)

Generate figures and axes to plot points corresponding to embeddings. This function can also be called and given figure and axes handeles. In that case the existing handles will be used to add the points and configure the axes and labels.

Parameters:

• loader (EmbedAndLabelLoader object) – contains the labels and embeddings by model, for quicker loading

• model_name (str) – name of model

• label_by (str, optional) – key of default_labels dict, by default “audio_file_name”

• paths (SimpleNamespace object, optional) – object with path attributes, defaults to None

• dim_reduction_model (str) – name of dim reduced model

• axes (plt object, optional) – axes handle, by default False

• fig (plt object, optional) – figure handle, by default False

• dashboard (bool, optional) – whether the calls comes from the dashboard, by deafult False

• dashboard_idx (int, optional) – index of dashboard plot, relevant for legend placement

Returns:

• plt object – axes handles is axes handles were given

• dict – color dictionary for legend

• list – plt point objects for legend of colorbar

bacpipe.embedding_evaluation.visualization.visualize_embeddings.plot_embeddings_px(embeds, labels, c_label_dict, label_by='label', **kwargs)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.reorder_embeddings_by_clustering_performance(plot_path, axes, models, order_metric='ground_truth-kmeans')

Reorder the embedding overview plot by clustering performance.

Parameters:

• plot_path (pathlib.Path object) – path to store plots and results comparing all models

• axes (plt.axes object) – handle for figures axes

• models (list) – list of models

• order_metric (str) – key corresponding to a metric in the clustering_results.json file. Defaults to “ARI(kmeans)”

bacpipe.embedding_evaluation.visualization.visualize_embeddings.return_rows_cols(num)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.set_colorbar_or_legend(fig, axes, points, c_label_dict, label_by, **kwargs)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.set_figsize_for_comparison(rows, cols)

bacpipe.embedding_evaluation.visualization.visualize_embeddings.set_legend(handles, labels, fig, axes, bool_plot_centroids=False, dashboard=False, **kwargs)

Create the legend for embeddings visualization plots.

Parameters:

• handles (list) – list of legend handles

• labels (list) – list of labels for legend

• fig (plt.fig object) – figure handle

• axes (plt.axes object) – axes handle

• bool_plot_centroids (bool, optional) – if True centroids of each class will be plotted, by default True

• dashboard (bool) – if dashboard called this function or not

Returns:

• plt.fig object – figure handle

• plt.axes object – axes handle