Merge pull request #384 from ReactiveBayes/gppl-4.4.0

Update deps to GraphPPL 4.4.0 and update docs on the visualization

Author: bvdmitri

Project.toml

@@ -1,7 +1,7 @@
 name = "RxInfer"
 uuid = "86711068-29c9-4ff7-b620-ae75d7495b3d"
 authors = ["Bagaev Dmitry <d.v.bagaev@tue.nl> and contributors"]
-version = "3.7.2"
+version = "3.8.0"
 
 [deps]
 BayesBase = "b4ee3484-f114-42fe-b91c-797d54a0c67e"
@@ -36,7 +36,7 @@ DomainSets = "0.5.2, 0.6, 0.7"
 ExponentialFamily = "1.5"
 ExponentialFamilyProjection = "1.1"
 FastCholesky = "1.3.0"
-GraphPPL = "~4.3.0"
+GraphPPL = "~4.4.0"
 LinearAlgebra = "1.9"
 MacroTools = "0.5.6"
 Optim = "1.0.0"

README.md

@@ -166,7 +166,7 @@ Our high-level project roadmap outlines the key milestones and focus areas for t
 
 | Q1/Q2 2024          | Q3/Q4 2024                | 2025                | 
 |---------------------|---------------------------|--------------------|
-| 🧩 **Nested models with [GraphPPL.jl](https://github.com/reactivebayes/GraphPPL.jl)** ✅    | 🌐 **Graph structure visualization** | 🔀 **Stochastic Processes** |
+| 🧩 **Nested models with [GraphPPL.jl](https://github.com/reactivebayes/GraphPPL.jl)** ✅    | 🌐 **Graph structure visualization** ✅ | 🔀 **Stochastic Processes** |
 | 🔄 **Development of [ExponentialFamilyProjection.jl]()** ✅                | 🧠 **Automated inference with [ExponentialFamilyProjection.jl](https://github.com/reactivebayes/ExponentialFamilyProjection.jl)** | 🚀 **Robustness & Memory-efficiency** |
 
 For a more granular view of our progress and ongoing tasks, check out our [project board](https://github.com/orgs/reactivebayes/projects/2/views/4) or join our 4-weekly [public meetings](https://dynalist.io/d/F4aA-Z2c8X-M1iWTn9hY_ndN).
@@ -195,7 +195,7 @@ RxInfer is a community-driven project and we welcome all contributions! To get s
 ## Active Inference Institute Collaboration
 
 The [Active Inference Institute](https://www.activeinference.org/) community members are enhancing RxInfer/GraphPPL's visualization capabilities. Their work includes:
-- Developing advanced model visualization features
+- Developing advanced model visualization features ✅ ([PR](https://github.com/ReactiveBayes/GraphPPL.jl/pull/251))
 - Creating summary and subgraph visualization modalities
 - Implementing various graph layout algorithms
 - Improving model inspection and understanding tools

codemeta.json

@@ -9,12 +9,12 @@
     "downloadUrl": "https://github.com/reactivebayes/RxInfer.jl/releases",
     "issueTracker": "https://github.com/reactivebayes/RxInfer.jl/issues",
     "name": "RxInfer.jl",
-    "version": "3.7.2",
+    "version": "3.8.0",
     "description": "Julia package for automated, scalable and efficient Bayesian inference on factor graphs with reactive message passing. ",
     "applicationCategory": "Statistics",
     "developmentStatus": "active",
     "readme": "https://reactivebayes.github.io/RxInfer.jl/stable/",
-    "softwareVersion": "3.7.2",
+    "softwareVersion": "3.8.0",
     "keywords": [
         "Bayesian inference",
         "message passing",

docs/Project.toml

@@ -8,6 +8,7 @@ ExponentialFamily = "62312e5e-252a-4322-ace9-a5f4bf9b357b"
 ExponentialFamilyProjection = "17f509fa-9a96-44ba-99b2-1c5f01f0931b"
 GraphPPL = "b3f8163a-e979-4e85-b43e-1f63d8c8b42c"
 GraphPlot = "a2cc645c-3eea-5389-862e-a155d0052231"
+GraphViz = "f526b714-d49f-11e8-06ff-31ed36ee7ee0"
 Optimisers = "3bd65402-5787-11e9-1adc-39752487f4e2"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 ReactiveMP = "a194aa59-28ba-4574-a09c-4a745416d6e3"

docs/src/manuals/model-specification.md

@@ -260,16 +260,45 @@ Specifically for the Gaussian/Normal case we have custom implementations that yi
 
 ## [Model structure visualisation](@id user-guide-model-specification-visualization)
 
-It is also possible to visualize the model structure after conditioning on data. For that we need two extra packages installed: `Cairo` and `GraphPlot`. Note, that those packages are not included in the `RxInfer` package and must be installed separately.
+Models specified using GraphPPL.jl in RxInfer.jl can be visualized in several ways to help understand their structure and relationships between variables. Let's create a simple model and visualize it.
 
-```@example model-specification-ssm
-using Cairo, GraphPlot
+```@example model-specification-visualization
+using RxInfer
+
+@model function coin_toss(y)
+    t ~ Beta(1, 1)
+    for i in eachindex(y)
+        y[i] ~ Bernoulli(t)
+    end
+end
+
+model_generator = coin_toss() | (y = [ true, false, true ], )
+model_to_plot   = RxInfer.getmodel(RxInfer.create_model(model_generator))
+nothing #hide
+```
 
-# `Create` the actual graph of the model conditioned on the data
-model = RxInfer.create_model(conditioned)
+### [GraphViz.jl](@id user-guide-model-specification-visualization-graphviz)
+
+It is possible to visualize the model structure after conditioning on data with the `GraphViz.jl` package.
+Note that this package is not included in the `RxInfer` package and must be installed separately.
+
+```@example model-specification-visualization
+using GraphViz
+
+# Call `load` function from `GraphViz` to visualise the structure of the graph
+GraphViz.load(model_to_plot, strategy = :simple)
+```
+
+### [Cairo](@id user-guide-model-specification-visualization-cairo)
+
+There is an alternative way to visuzalise the model structure with `Cairo` and `GraphPlot`
+Note, that those packages are also not included in the `RxInfer` package and must be installed separately.
+
+```@example model-specification-visualization
+using Cairo, GraphPlot
 
 # Call `gplot` function from `GraphPlot` to visualise the structure of the graph
-GraphPlot.gplot(RxInfer.getmodel(model))
+GraphPlot.gplot(model_to_plot)
 ```
 
 ## Node Contraction