Refactor usage examples to use Compose for plotting

docs/Project.toml

@@ -1,10 +1,14 @@
 [deps]
 BayesNets = "ba4760a4-c768-5bed-964b-cf806dc591cb"
+Compose = "a81c6b42-2e10-5240-aca2-a61377ecd94b"
+DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Discretizers = "6e83dbb3-75ca-525b-8ae2-3751f0dd50b4"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 RDatasets = "ce6b1742-4840-55fa-b093-852dadbb1d8b"
 TikzGraphs = "b4f28e30-c73f-5eaf-a395-8a9db949a742"
 TikzPictures = "37f6aa50-8035-52d0-81c2-5a1d08754b2d"
 
 [compat]
-Documenter = "1"
+Documenter = "1"

docs/src/usage.md

@@ -1,15 +1,17 @@
+```@meta
+CurrentModule = BayesNets
+```
 # Usage
 
 ```@setup bayesnet
-using BayesNets, TikzGraphs, TikzPictures
-```
-
-```julia
-using Random
-Random.seed!(0) # seed the random number generator to 0, for a reproducible demonstration
 using BayesNets
-using TikzGraphs # required to plot tex-formatted graphs (recommended), otherwise GraphPlot.jl is used
-using TikzPictures
+using Compose
+import Compose: SVG
+using DataFrames
+using Distributions
+using Random
+using Graphs
+Random.seed!(0)
 ```
 
 ## Representation
@@ -25,8 +27,8 @@ a = \mathcal{N}(0,1) \qquad b = \mathcal{N}(2a +3,1)
 bn = BayesNet()
 push!(bn, StaticCPD(:a, Normal(1.0)))
 push!(bn, LinearGaussianCPD(:b, [:a], [2.0], 3.0, 1.0))
-plot = BayesNets.plot(bn)
-TikzPictures.save(SVG("plot1"), plot)
+p = BayesNets.plot(bn)
+draw(SVG(joinpath(@__DIR__, "plot1.svg"), 400, 400), p)
 ```
 
 ![](plot1.svg)
@@ -56,8 +58,8 @@ cpdA = fit(StaticCPD{Normal}, data, :a)
 cpdB = fit(LinearGaussianCPD, data, :b, [:a])
 
 bn2 = BayesNet([cpdA, cpdB])
-plot = BayesNets.plot(bn2) # hide
-TikzPictures.save(SVG("plot2"), plot) # hide
+p = BayesNets.plot(bn2)
+draw(SVG(joinpath(@__DIR__, "plot2.svg"), 400, 400), p)
 ```
 
 ![](plot2.svg)
@@ -66,7 +68,7 @@ Each `CPD` implements four functions:
 
 * `name(cpd)` - obtain the name of the variable target variable
 * `parents(cpd)` - obtain the list of parents
-* `nparams(cpd` - obtain the number of free parameters in the CPD
+* `nparams(cpd)` - obtain the number of free parameters in the CPD
 * `cpd(assignment)` - allows calling `cpd()` to obtain the conditional distribution
 * `Distributions.fit(Type{CPD}, data, target, parents)`
 
@@ -88,8 +90,8 @@ The NamedCategorical distribution allows for String or Symbol return values. The
 bn2 = BayesNet()
 push!(bn2, StaticCPD(:sighted, NamedCategorical([:bird, :plane, :superman], [0.40, 0.55, 0.05])))
 push!(bn2, FunctionalCPD{Bernoulli}(:happy, [:sighted], a->Bernoulli(a == :superman ? 0.95 : 0.2)))
-plot = BayesNets.plot(bn2) # hide
-TikzPictures.save(SVG("plot3"), plot) # hide
+p = BayesNets.plot(bn2)
+draw(SVG(joinpath(@__DIR__, "plot3.svg"), 400, 400), p)
 ```
 
 ![](plot3.svg)
@@ -98,8 +100,8 @@ Variables can be removed by name using `delete!`. A warning will be issued when
 
 ```@example bayesnet
 delete!(bn2, :happy)
-plot = BayesNets.plot(bn2) # hide
-TikzPictures.save(SVG("plot4"), plot) # hide
+p = BayesNets.plot(bn2)
+draw(SVG(joinpath(@__DIR__, "plot4.svg"), 400, 400), p)
 ```
 
 ![](plot4.svg)
@@ -153,8 +155,8 @@ bn = BayesNet()
 push!(bn, StaticCPD(:a, Categorical([0.3,0.7])))
 push!(bn, StaticCPD(:b, Categorical([0.6,0.4])))
 push!(bn, CategoricalCPD{Bernoulli}(:c, [:a, :b], [2,2], [Bernoulli(0.1), Bernoulli(0.2), Bernoulli(1.0), Bernoulli(0.4)]))
-plot = BayesNets.plot(bn) # hide
-TikzPictures.save(SVG("plot5"), plot) # hide
+p = BayesNets.plot(bn)
+draw(SVG(joinpath(@__DIR__, "plot5.svg"), 400, 400), p)
 ```
 
 ![](plot5.svg)
@@ -202,8 +204,8 @@ b=[1,1,1,2,2,2,2,1,1,2,1,1],
 a=[1,1,1,2,1,1,2,1,1,2,1,1])
 
 bn5 = fit(DiscreteBayesNet, data, (:a=>:b, :a=>:c, :b=>:c))
-plot = BayesNets.plot(bn5) # hide
-TikzPictures.save(SVG("plot6"), plot) # hide
+p = BayesNets.plot(bn5)
+draw(SVG(joinpath(@__DIR__, "plot6.svg"), 400, 400), p)
 ```
 
 ![](plot6.svg)
@@ -230,8 +232,8 @@ push!(bn, DiscreteCPD(:c, [:a, :b], [2,2],
          Categorical([0.4,0.6]),
         ]))
 
-plot = BayesNets.plot(bn) # hide
-TikzPictures.save(SVG("plot7"), plot) # hide
+p = BayesNets.plot(bn)
+draw(SVG(joinpath(@__DIR__, "plot7.svg"), 400, 400), p)
 ```
 
 ![](plot7.svg)
@@ -288,8 +290,8 @@ parameters = K2GraphSearch([:Species, :SepalLength, :SepalWidth, :PetalLength, :
                        max_n_parents=2)
 bn = fit(BayesNet, data, parameters)
 
-plot = BayesNets.plot(bn) # hide
-TikzPictures.save(SVG("plot8"), plot) # hide
+p = BayesNets.plot(bn)
+draw(SVG(joinpath(@__DIR__, "plot8.svg"), 400, 400), p)
 ```
 
 ![](plot8.svg)
@@ -317,8 +319,8 @@ data = DataFrame(c=[1,1,1,1,2,2,2,2,3,3,3,3],
 parameters = GreedyHillClimbing(ScoreComponentCache(data), max_n_parents=3, prior=UniformPrior())
 bn = fit(DiscreteBayesNet, data, parameters)
 
-plot = BayesNets.plot(bn) # hide
-TikzPictures.save(SVG("plot9"), plot) # hide
+p = BayesNets.plot(bn)
+draw(SVG(joinpath(@__DIR__, "plot9.svg"), 400, 400), p)
 ```
 
 ![](plot9.svg)
@@ -358,8 +360,8 @@ Discrete Bayesian Networks can be read from the .XDSL file format.
 ```@example bayesnet
 bn = readxdsl(joinpath(dirname(pathof(BayesNets)), "..", "test", "sample_bn.xdsl"))
 
-plot = BayesNets.plot(bn) # hide
-TikzPictures.save(SVG("plot10"), plot) # hide
+p = BayesNets.plot(bn)
+draw(SVG(joinpath(@__DIR__, "plot10.svg"), 400, 400), p)
 ```
 
 ![](plot10.svg)
@@ -374,5 +376,5 @@ data = DataFrame(c=[1,1,1,1,2,2,2,2,3,3,3,3],
                  a=[1,1,1,2,1,1,2,1,1,2,1,1])
 g = DAG(3)
 add_edge!(g,1,2); add_edge!(g,2,3); add_edge!(g,1,3)
-bayesian_score(g, [:a,:b,:c], data)
+BayesNets.bayesian_score(g, [:a, :b, :c], data)
 ```