update generic tensor network to version 2.0 (#92)

* update gtn

* fix docs

* fix makefile

* update

Author: GiggleLiu

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 = "0.4.2"
+version = "0.4.3"
 
 [deps]
 Artifacts = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
@@ -17,10 +17,13 @@ StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 TropicalNumbers = "b3a74e9c-7526-4576-a4eb-79c0d4c32334"
 
 [compat]
+Artifacts = "1"
 CUDA = "4, 5"
 DocStringExtensions = "0.8.6, 0.9"
-GenericTensorNetworks = "1"
-OMEinsum = "0.7"
+GenericTensorNetworks = "2"
+LinearAlgebra = "1"
+OMEinsum = "0.8"
+Pkg = "1"
 PrecompileTools = "1"
 Requires = "1"
 StatsBase = "0.34"

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)
 

src/generictensornetworks.jl

@@ -12,16 +12,15 @@ Convert a constraint satisfiability problem (or energy model) to a probabilistic
 * `problem` is a `GraphProblem` instance in [`GenericTensorNetworks`](https://github.com/QuEraComputing/GenericTensorNetworks.jl).
 * `β` is the inverse temperature.
 """
-function TensorInference.TensorNetworkModel(problem::GraphProblem, β::Real; evidence::Dict=Dict{Int,Int}(),
-        optimizer=GreedyMethod(), simplifier=nothing, mars=[[l] for l in labels(problem)])
-	ixs = getixsv(problem.code)
-	iy = getiyv(problem.code)
+function TensorInference.TensorNetworkModel(problem::GraphProblem, β::Real; evidence::Dict=Dict{eltype(labels(problem)),Int}(),
+        optimizer=GreedyMethod(), openvars=empty(labels(problem)), simplifier=nothing, mars=[[l] for l in labels(problem)])
+	ixs = [GenericTensorNetworks.energy_terms(problem)..., GenericTensorNetworks.extra_terms(problem)...]
     lbs = labels(problem)
 	nflavors = length(flavors(problem))
 	# generate tensors for x = e^β
 	tensors = generate_tensors(exp(β), problem)
     factors = [Factor((ix...,), t) for (ix, t) in zip(ixs, tensors)]
-	return TensorNetworkModel(lbs, fill(nflavors, length(lbs)), factors; openvars=iy, evidence, optimizer, simplifier, mars)
+	return TensorNetworkModel(lbs, fill(nflavors, length(lbs)), factors; openvars, evidence, optimizer, simplifier, mars)
 end
 
 """
@@ -43,18 +42,18 @@ end
 
 function TensorInference.MMAPModel(problem::GraphProblem, β::Real;
             queryvars,
-            evidence = Dict{labeltype(problem.code), Int}(),
+            openvars = empty(labels(problem)),
+            evidence = Dict{eltype(labels(problem)), Int}(),
             optimizer = GreedyMethod(), simplifier = nothing,
             marginalize_optimizer = GreedyMethod(), marginalize_simplifier = nothing
         )::MMAPModel
-    ixs = getixsv(problem.code)
-    iy = getiyv(problem.code)
+	ixs = [GenericTensorNetworks.energy_terms(problem)..., GenericTensorNetworks.extra_terms(problem)...]
 	nflavors = length(flavors(problem))
 	# generate tensors for x = e^β
 	tensors = generate_tensors(exp(β), problem)
     factors = [Factor((ix...,), t) for (ix, t) in zip(ixs, tensors)]
     lbs = labels(problem)
-    return MMAPModel(lbs, fill(nflavors, length(lbs)), factors; queryvars, openvars=iy, evidence,
+    return MMAPModel(lbs, fill(nflavors, length(lbs)), factors; queryvars, openvars, evidence,
         optimizer, simplifier,
         marginalize_optimizer, marginalize_simplifier)
 end

test/generictensornetworks.jl

@@ -8,8 +8,8 @@ using GenericTensorNetworks, TensorInference
     problem = IndependentSet(g)
     model = TensorNetworkModel(problem, β; mars=[[2, 3]])
     mars = marginals(model)[[2, 3]]
-    problem2 = IndependentSet(g; openvertices=[2,3])
-    mars2 = TensorInference.normalize!(GenericTensorNetworks.solve(problem2, PartitionFunction(β)), 1)
+    problem2 = IndependentSet(g)
+    mars2 = TensorInference.normalize!(GenericTensorNetworks.solve(GenericTensorNetwork(problem2; openvertices=[2, 3]), PartitionFunction(β)), 1)
     @test mars ≈ mars2
 
     # update temperature