morphoclass.xai.model_attributions module

Explain model layers using GradShap.

morphoclass.xai.model_attributions.cnn_model_attributions(model, dataset, sample_id, interpretability_method_cls)

Explain CNN model.

Plot with feature maps after each feature extractor layer. Starting from original image to the last featrue extractor layer. Only one morphology sample is visualized.

Parameters

• model (morphoclass.models.cnnet.CNNet) – Model that will be explained.

• dataset (morphoclass.data.morphology_dataset.MorphologyDataset) – Dataset containing embeddings and morphologies.

• sample_id (int) – The id of embedding in the dataset.

• interpretability_method_cls – An interpretability class from captum.attr.

Returns

fig – Figure with explainable plots.

Return type

matplotlib.figure.Figure

morphoclass.xai.model_attributions.cnn_model_attributions_population(model, dataset)

Generate the SHAP explanation for a population of neurons.

morphoclass.xai.model_attributions.get_edges_colors_based_on_barcode_colors(tree, colors)

Collect colors for edges based on barcode colors.

Parameters

• tree (tmd.Tree.Tree) – Morphology tree used to create barcode.

• colors (list_like) – List of barcode colors.

Returns

color_edges – List of edge colors.

Return type

list_like

morphoclass.xai.model_attributions.gnn_model_attributions(model, dataset, sample_id, interpretability_method_cls)

Explain GNN model.

Plot with two rows:

• Original graph and graph with GradShap values within the nodes.

• Heatmap of the original graph (zero-values) and heatmap of the GradShap values on the graph.

Only one morphology sample is visualized.

Parameters

• model (morphoclass.models.man_net.ManNet) – Model that will be explained.

• dataset (morphoclass.data.morphology_dataset.MorphologyDataset) – Dataset containing morphologies.

• sample_id (int) – The id of morphology in the dataset.

Returns

fig – Figure with explainable plots.

Return type

matplotlib.figure.Figure

morphoclass.xai.model_attributions.perslay_model_attributions(model, dataset, sample_id, interpretability_method_cls)

Explain PersLay model.

Plot with 3 rows:

• Barcodes: The original barcode and GradShap weighted barcode (colored bar) after each feature extraction layer.

• Persistence diagrams: The original PD and GradShap weighted PD (colored dot) after each feature extraction layer.

• Graph: The original graph and GradShap weighted graph (colored edge) after each feature extraction layer.

Parameters

• model (morphoclass.models.coriander_net.CorianderNet) – Model that will be explained.

• dataset (morphoclass.data.morphology_dataset.MorphologyDataset) – Dataset containing embeddings and morphologies.

• sample_id (int) – The id of embedding in the dataset.

• interpretability_method_cls – An interpretability class from captum.attr.

Returns

fig – A figure with explainable plots.

Return type

matplotlib.figure.Figure

morphoclass.xai.model_attributions.sklearn_model_attributions_shap(model, dataset, sample_id)

Explain sklearn model.

Plot with feature maps after each feature extractor layer. Starting from original image to the last feature extractor layer. Only one morphology sample is visualized.

Parameters

• model – Model that will be explained.

• dataset (morphoclass.data.morphology_dataset.MorphologyDataset) – Dataset containing embeddings and morphologies.

• sample_id (int) – The id of embedding in the dataset.

Returns

fig – A figure with explainable plots.

Return type

matplotlib.figure.Figure

morphoclass.xai.model_attributions.sklearn_model_attributions_tree(model, dataset)

Explain sklearn tree model.

Parameters

• model – Model that will be explained.

• dataset (morphoclass.data.morphology_dataset.MorphologyDataset) – Dataset containing embeddings and morphologies.

Returns

fig – Figure with explainable plots.

Return type

matplotlib.figure.Figure