gfn.gym.helpers.bayesian_structure.evaluation
=============================================

.. py:module:: gfn.gym.helpers.bayesian_structure.evaluation

.. autoapi-nested-parse::

   The code is adapted from:
   https://github.com/larslorch/dibs/blob/master/dibs/metrics.py



Functions
---------

.. autoapisummary::

   gfn.gym.helpers.bayesian_structure.evaluation.expected_edges
   gfn.gym.helpers.bayesian_structure.evaluation.expected_shd
   gfn.gym.helpers.bayesian_structure.evaluation.posterior_estimate
   gfn.gym.helpers.bayesian_structure.evaluation.threshold_metrics


Module Contents
---------------

.. py:function:: expected_edges(posterior_samples)

   Compute the expected number of edges.

   This function computes the expected number of edges in graphs sampled from
   the posterior approximation.

   :param posterior_samples: Samples from the posterior. The tensor must have size
                             `(B, N, N)`, where `B` is the number of sample graphs from the posterior
                             approximation, and `N` is the number of variables in the graphs.

   :returns: The expected number of edges.
   :rtype: e_edges


.. py:function:: expected_shd(posterior_samples, gt_graph)

   Compute the Expected Structural Hamming Distance.

   This function computes the Expected SHD between a posterior approximation
   given as a collection of samples from the posterior, and the ground-truth
   graph used in the original data generation process.

   :param posterior_samples: Samples from the posterior. The tensor must have size
                             `(B, N, N)`, where `B` is the number of sample graphs from the posterior
                             approximation, and `N` is the number of variables in the graphs.
   :param gt_graph: GeometricData instance representing the ground-truth graph.

   :returns: The Expected SHD.
   :rtype: e_shd


.. py:function:: posterior_estimate(gflownet, env, num_samples=1000, batch_size=100, verbose=True)

   Get the posterior estimate of DAG-GFlowNet as a collection of graphs
   sampled from the GFlowNet.

   :param gflownet: `GFlowNet` instance.
   :param env: `BayesianStructure` environment.
   :param rng: Optional random generator instance.
   :param num_samples: The number of samples in the posterior approximation.
   :param verbose: If True, display a progress bar for the sampling process.

   :returns:

             torch.Tensor with shape `(B, N, N)`, where `B` is the number of sample
                 graphs in the posterior approximation, and `N` is the number of variables
                 in a graph.
   :rtype: posterior


.. py:function:: threshold_metrics(posterior_samples, gt_graph)

   Compute threshold metrics (e.g. AUROC, Precision, Recall, etc...).

   :param posterior_samples: Samples from the posterior. The tensor must have size
                             `(B, N, N)`, where `B` is the number of sample graphs from the posterior
                             approximation, and `N` is the number of variables in the graphs.
   :param gt_graph: GeometricData instance representing the ground-truth graph.

   :returns:     - False Positive Rate
                 - True Positive Rate
                 - Area Under the Receiver Operating Characteristic Curve
                 - Precision
                 - Recall
                 - Area Under the Precision-Recall Curve
                 - Average Precision
   :rtype: A dictionary containing the following metrics