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torch_geometric.explain

docs/source/modules/explain.rst

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torch_geometric.explain

.. currentmodule:: torch_geometric.explain

.. warning::

This module is in active development and may not be stable.
Access requires installing :pyg:`PyG` from master.

.. contents:: Contents :local:

Philosophy

This module provides a set of tools to explain the predictions of a PyG model or to explain the underlying phenomenon of a dataset (see the "GraphFramEx: Towards Systematic Evaluation of Explainability Methods for Graph Neural Networks" <https://arxiv.org/abs/2206.09677>_ paper for more details).

We represent explanations using the :class:torch_geometric.explain.Explanation class, which is a :class:~torch_geometric.data.Data object containing masks for the nodes, edges, features and any attributes of the data.

The :class:torch_geometric.explain.Explainer class is designed to handle all explainability parameters (see the :class:torch_geometric.explain.config.ExplainerConfig class for more details):

  • which algorithm from the :class:torch_geometric.explain.algorithm module to use (e.g., :class:~torch_geometric.explain.algorithm.GNNExplainer)
  • the type of explanation to compute (e.g., :obj:explanation_type="phenomenon" or :obj:explanation_type="model")
  • the different type of masks for node and edges (e.g., :obj:mask="object" or :obj:mask="attributes")
  • any postprocessing of the masks (e.g., :obj:threshold_type="topk" or :obj:threshold_type="hard")

This class allows the user to easily compare different explainability methods and to easily switch between different types of masks, while making sure the high-level framework stays the same.

Explainer

.. autoclass:: torch_geometric.explain.Explainer :show-inheritance: :members: :special-members: call

.. autoclass:: torch_geometric.explain.config.ExplainerConfig :members:

.. autoclass:: torch_geometric.explain.config.ModelConfig :members:

.. autoclass:: torch_geometric.explain.config.ThresholdConfig :members:

Explanations

.. autoclass:: torch_geometric.explain.Explanation :show-inheritance: :members:

.. autoclass:: torch_geometric.explain.HeteroExplanation :show-inheritance: :members:

Explainer Algorithms

.. currentmodule:: torch_geometric.explain.algorithm

.. autosummary:: :nosignatures: :toctree: ../generated

{% for name in torch_geometric.explain.algorithm.classes %} {{ name }} {% endfor %}

Explanation Metrics

The quality of an explanation can be judged by a variety of different methods. PyG supports the following metrics out-of-the-box:

.. currentmodule:: torch_geometric.explain.metric

.. autosummary:: :nosignatures: :toctree: ../generated

{% for name in torch_geometric.explain.metric.classes %} {{ name }} {% endfor %}