Overview
Introduction
What is GSNN?
Key Concepts
Why Use GSNN?
Core Features
How Are GSNNs Different from Graph Neural Networks?
Getting Started
Installation
Citation
Next Steps
Methods
Graph Structured Neural Network (GSNN)
Explainers
Edge Attribution Methods
Direct Perturbation Methods
Optimization-Based Methods
Robustness and Stability Methods
Choosing the Right Explainer
Tutorials
General Premise
Simulating structured data
Performance comparison on simulated data
Reinforcement learning for structure optimization
Gradient checkpointing and compiling
Uncertainty quantification with hypernetworks
GSNN Interpretation methods
DrugCell implementation example
Inferring output edges
Pathway latent factor regularization
Function-node activity gating
Per-channel function-node activity gating
Inferring function-function edges (Tier 0)
Online edge inference via auxiliary regression (Tier 0+)
Function Edge Inferer
Post-hoc edge inference via shared-embedding link prediction (node2vec)
API Reference
API Reference
Graph Structured Neural Networks
gsnn.gsnn.optim.REINFORCE
View page source
gsnn.gsnn.optim.REINFORCE
Classes
REINFORCE
(*args, **kwargs)
class
gsnn.gsnn.optim.REINFORCE.
REINFORCE
(
*
args
:
Any
,
**
kwargs
:
Any
)
[source]
Bases:
Module
get_edge_probs
(
)
[source]
get_reward_params
(
)
[source]
print_progress_
(
)
[source]
prob_of
(
action
)
[source]
sample
(
)
[source]
scale
(
rewards
)
[source]
step
(
)
[source]
update
(
rewards
,
actions
=
None
)
[source]