make ProblemReductions a dependency

Author: GiggleLiu

CITATION.bib

@@ -0,0 +1,12 @@
+@article{Roa-Villescas2023, 
+  doi = {10.21105/joss.05700},
+  url = {https://doi.org/10.21105/joss.05700},
+  year = {2023},
+  publisher = {The Open Journal},
+  volume = {8},
+  number = {90},
+  pages = {5700},
+  author = {Martin Roa-Villescas and Jin-Guo Liu},
+  title = {TensorInference: A Julia package for tensor-based probabilistic inference},
+  journal = {Journal of Open Source Software}
+}

Makefile

@@ -0,0 +1,24 @@
+JL = julia --project
+
+default: init test
+
+init:
+	$(JL) -e 'using Pkg; Pkg.precompile(); Pkg.activate("docs"); Pkg.develop(path="."), Pkg.precompile()'
+
+update:
+	$(JL) -e 'using Pkg; Pkg.update(); Pkg.precompile(); Pkg.activate("docs"); Pkg.update(); Pkg.precompile()'
+
+test:
+	$(JL) -e 'using Pkg; Pkg.test()'
+
+coverage:
+	$(JL) -e 'using Pkg; Pkg.test(; coverage=true)'
+
+serve:
+	$(JL) -e 'using Pkg; Pkg.activate("docs"); using LiveServer; servedocs(;skip_dirs=["docs/src/assets", "docs/src/generated"], literate_dir="examples")'
+
+clean:
+	rm -rf docs/build
+	find . -name "*.cov" -type f -print0 | xargs -0 /bin/rm -f
+
+.PHONY: init test coverage serve clean update

Project.toml

@@ -1,7 +1,7 @@
 name = "TensorInference"
 uuid = "c2297e78-99bd-40ad-871d-f50e56b81012"
 authors = ["Jin-Guo Liu", "Martin Roa Villescas"]
-version = "0.4.0"
+version = "0.4.2"
 
 [deps]
 Artifacts = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
@@ -10,23 +10,27 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 OMEinsum = "ebe7aa44-baf0-506c-a96f-8464559b3922"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 PrecompileTools = "aea7be01-6a6a-4083-8856-8a6e6704d82a"
+PrettyTables = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
+ProblemReductions = "899c297d-f7d2-4ebf-8815-a35996def416"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 TropicalNumbers = "b3a74e9c-7526-4576-a4eb-79c0d4c32334"
 
 [weakdeps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-GenericTensorNetworks = "3521c873-ad32-4bb4-b63d-f4f178f42b49"
 
 [extensions]
 TensorInferenceCUDAExt = "CUDA"
-TensorInferenceGTNExt = "GenericTensorNetworks"
 
 [compat]
+Artifacts = "1"
 CUDA = "4, 5"
 DocStringExtensions = "0.8.6, 0.9"
-GenericTensorNetworks = "1"
-OMEinsum = "0.7"
+LinearAlgebra = "1"
+OMEinsum = "0.8"
+Pkg = "1"
 PrecompileTools = "1"
+PrettyTables = "2"
+ProblemReductions = "0.3"
 StatsBase = "0.34"
 TropicalNumbers = "0.5.4, 0.6"
 julia = "1.9"

README.md

@@ -51,7 +51,7 @@ comprehensive introduction to the package read the
 ## Citing
 
 If you use TensorInference as part of your research, teaching, or other
-activities, please consider citing the following publication: [(TBA)]().
+activities, please consider [citing our work](CITATION.bib).
 
 ## Questions and Contributions
 

benchmark/bench_map.jl

@@ -6,7 +6,7 @@ using Artifacts
 
 const SUITE = BenchmarkGroup()
 
-problem = problem_from_artifact("uai2014", "MAR" "Promedus", 14)
+problem = problem_from_artifact("uai2014", "MAR", "Promedus", 14)
 
 optimizer = TreeSA(ntrials = 1, niters = 2, βs = 1:0.1:40)
 tn = TensorNetworkModel(read_model(problem); optimizer, evidence=get_evidence(problem))

docs/Project.toml

@@ -3,5 +3,6 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 GenericTensorNetworks = "3521c873-ad32-4bb4-b63d-f4f178f42b49"
 Literate = "98b081ad-f1c9-55d3-8b20-4c87d4299306"
 LiveServer = "16fef848-5104-11e9-1b77-fb7a48bbb589"
+ProblemReductions = "899c297d-f7d2-4ebf-8815-a35996def416"
 TensorInference = "c2297e78-99bd-40ad-871d-f50e56b81012"
 TikzPictures = "37f6aa50-8035-52d0-81c2-5a1d08754b2d"

docs/src/api/public.md

@@ -68,4 +68,5 @@ read_td_file
 sample
 update_evidence!
 update_temperature
+random_matrix_product_state
 ```

examples/hard-core-lattice-gas/main.jl

@@ -15,16 +15,16 @@
 
 a, b = (1, 0), (0.5, 0.5*sqrt(3))
 Na, Nb = 10, 10
-sites = [a .* i .+ b .* j for i=1:Na, j=1:Nb]
+sites = vec([50 .* (a .* i .+ b .* j) for i=1:Na, j=1:Nb])
 
 # There exists blockade interactions between hard-core particles.
 # We connect two lattice sites within blockade radius by an edge.
 # Two ends of an edge can not both be occupied by particles.
-blockade_radius = 1.1
+blockade_radius = 55
 using GenericTensorNetworks: show_graph, unit_disk_graph
 using GenericTensorNetworks.Graphs: edges, nv
 graph = unit_disk_graph(vec(sites), blockade_radius)
-show_graph(graph; locs=sites, texts=fill("", length(sites)))
+show_graph(graph, sites; texts=fill("", length(sites)))
 
 # These constraints defines an independent set problem that characterized by the following energy based model.
 # Let $G = (V, E)$ be a graph, where $V$ is the set of vertices and $E$ is the set of edges.
@@ -39,7 +39,7 @@ show_graph(graph; locs=sites, texts=fill("", length(sites)))
 # The independent set problem involves finding a set of vertices in a graph such that no two vertices in the set are adjacent (i.e., there is no edge connecting them).
 # One can create a tensor network based modeling of an independent set problem with package [`GenericTensorNetworks.jl`](https://github.com/QuEraComputing/GenericTensorNetworks.jl).
 using GenericTensorNetworks
-problem = IndependentSet(graph; optimizer=GreedyMethod());
+problem = IndependentSet(graph)
 
 # There are plenty of discussions related to solution space properties in the `GenericTensorNetworks` [documentaion page](https://queracomputing.github.io/GenericTensorNetworks.jl/dev/generated/IndependentSet/).
 # In this example, we show how to use `TensorInference` to use probabilistic inference for understand the finite temperature properties of this statistical model.
@@ -59,15 +59,14 @@ partition_func[]
 
 # The marginal probabilities can be computed with the [`marginals`](@ref) function, which measures how likely a site is occupied.
 mars = marginals(pmodel)
-show_graph(graph; locs=sites, vertex_colors=[(b = mars[[i]][2]; (1-b, 1-b, 1-b)) for i in 1:nv(graph)], texts=fill("", nv(graph)))
+show_graph(graph, sites; vertex_colors=[(b = mars[[i]][2]; (1-b, 1-b, 1-b)) for i in 1:nv(graph)], texts=fill("", nv(graph)))
 # The can see the sites at the corner is more likely to be occupied.
 # To obtain two-site correlations, one can set the variables to query marginal probabilities manually.
 pmodel2 = TensorNetworkModel(problem, β; mars=[[e.src, e.dst] for e in edges(graph)])
 mars = marginals(pmodel2);
 
 # We show the probability that both sites on an edge are not occupied
-show_graph(graph; locs=sites, edge_colors=[(b = mars[[e.src, e.dst]][1, 1]; (1-b, 1-b, 1-b)) for e in edges(graph)], texts=fill("", nv(graph)),
-    edge_line_widths=edge_colors=[8*mars[[e.src, e.dst]][1, 1] for e in edges(graph)])
+show_graph(graph, sites; edge_colors=[(b = mars[[e.src, e.dst]][1, 1]; (1-b, 1-b, 1-b)) for e in edges(graph)], texts=fill("", nv(graph)), config=GraphDisplayConfig(; edge_line_width=5))
 
 # ## The most likely configuration
 # The MAP and MMAP can be used to get the most likely configuration given an evidence.
@@ -78,7 +77,7 @@ mars = marginals(pmodel3)
 logp, config = most_probable_config(pmodel3)
 
 # The log probability is 102. Let us visualize the configuration.
-show_graph(graph; locs=sites, vertex_colors=[(1-b, 1-b, 1-b) for b in config], texts=fill("", nv(graph)))
+show_graph(graph, sites; vertex_colors=[(1-b, 1-b, 1-b) for b in config], texts=fill("", nv(graph)))
 # The number of particles is
 sum(config)
 
@@ -87,7 +86,7 @@ pmodel3 = TensorNetworkModel(problem, β; evidence=Dict(1=>0))
 logp2, config2 = most_probable_config(pmodel)
 
 # The log probability is 99, which is much smaller.
-show_graph(graph; locs=sites, vertex_colors=[(1-b, 1-b, 1-b) for b in config2], texts=fill("", nv(graph)))
+show_graph(graph, sites; vertex_colors=[(1-b, 1-b, 1-b) for b in config2], texts=fill("", nv(graph)))
 # The number of particles is
 sum(config2)
 

ext/TensorInferenceGTNExt.jl

@@ -175,6 +175,21 @@ function TensorNetworkModel(
     TensorNetworkModel(collect(LT, vars), code, tensors, evidence, mars)
 end
 
+"""
+$(TYPEDSIGNATURES)
+"""
+function TensorNetworkModel(
+    model::UAIModel{T}, code;
+    evidence = Dict{Int,Int}(),
+    mars = [[i] for i=1:model.nvars],
+    vars = [1:model.nvars...]
+)::TensorNetworkModel where{T}
+    @debug "constructing tensor network model from code"
+    tensors = Array{T}[[ones(T, [model.cards[i] for i in mar]...) for mar in mars]..., [t.vals for t in model.factors]...]
+
+    return TensorNetworkModel(vars, code, tensors, evidence, mars)
+end
+
 """
 $(TYPEDSIGNATURES)
 
@@ -189,7 +204,8 @@ Get the cardinalities of variables in this tensor network.
 """
 function get_cards(tn::TensorNetworkModel; fixedisone = false)::Vector
     vars = get_vars(tn)
-    [fixedisone && haskey(tn.evidence, vars[k]) ? 1 : length(tn.tensors[k]) for k in eachindex(vars)]
+    size_dict = OMEinsum.get_size_dict(getixsv(tn.code), tn.tensors)
+    [fixedisone && haskey(tn.evidence, vars[k]) ? 1 : size_dict[vars[k]] for k in eachindex(vars)]
 end
 
 chevidence(tn::TensorNetworkModel, evidence) = TensorNetworkModel(tn.vars, tn.code, tn.tensors, evidence)