Docs+workflows

.github/workflows/Docs.yml

@@ -50,9 +50,9 @@ jobs:
         run: |
           julia --project=docs -e '
             using Documenter: DocMeta, doctest
-            using ARSampling
-            DocMeta.setdocmeta!(ARSampling, :DocTestSetup, :(using ARSampling); recursive=true)
-            doctest(ARSampling)'
+            using AdaptiveRejectionSampling
+            DocMeta.setdocmeta!(AdaptiveRejectionSampling, :DocTestSetup, :(using AdaptiveRejectionSampling); recursive=true)
+            doctest(AdaptiveRejectionSampling)'
       - name: Generate and deploy documentation
         run: julia --project=docs docs/make.jl
         env:

.gitignore

@@ -2,3 +2,4 @@
 *.jl.*.cov
 *.jl.mem
 Manifest.toml
+**/build/

README.md

@@ -12,9 +12,13 @@ This package is useful for efficient sampling from log-concave univariate densit
 ## Examples
 
 ```julia
-using AdaptiveRejectionSampling: Objective, ARSampler, sample!, eval_hull, abscissae
+# Import packages and setup
+using AdaptiveRejectionSampling: Objective, ARSampler, sample!, eval_hull, abscissae, hullplot!
 using SpecialFunctions: gamma
 using CairoMakie
+using Chairmarks
+
+set_theme!(theme_minimal())
 ```
 
 ### Sampling from a shifted normal distribution
@@ -31,18 +35,18 @@ function bench()
     # Build the sampler and simulate 10,000 samples
     obj = Objective(f)
     sampler = ARSampler(obj, support)
-    b = @be deepcopy(sampler) sample!(_, 10000, true, 10);
+    b = @be deepcopy(sampler) sample!(_, 10000, max_segments = 10);
 return sampler, b
 end
 s, b = bench();
 b
 ```
 
-      Benchmark: 126 samples with 1 evaluation
-      min    616.951 μs (17 allocs: 80.523 KiB)
-      median 641.532 μs (17 allocs: 80.523 KiB)
-      mean   749.812 μs (17.10 allocs: 80.625 KiB, 0.75% gc time)
-      max    13.744 ms (20 allocs: 83.711 KiB, 94.06% gc time)
+    Benchmark: 166 samples with 1 evaluation
+     min    535.050 μs (3 allocs: 78.195 KiB)
+     median 565.130 μs (6 allocs: 81.852 KiB)
+     mean   571.223 μs (5.73 allocs: 81.521 KiB)
+     max    740.072 μs (6 allocs: 81.852 KiB)
 
 Let's verify the result
 
@@ -55,15 +59,10 @@ envelop = eval_hull.(s.upper_hull, x)
 target = f.(x)
 samples = sample!(s, 10000, max_segments = 5)
 
-fig, ax, p = hist(samples, bins=25, normalization = :pdf, label = "Samples");
-lines!(ax, -10..10, x -> exp(f(x)), label = "Target", color = :orange)
-lines!(ax, x, exp.(envelop), label = "Envelop", color = :black, alpha = 0.8)
-absc = ARS.abscissae(s.upper_hull)
-scatter!(ax, absc, exp.(eval_hull.(s.upper_hull, absc)), label = "Abscissae", color = :red)
+fig, ax, p = hist(samples, bins=25, color = :grey, normalization = :pdf, label = "Samples");
+hullplot!(ax, -10..10, s, target = true)
 axislegend(ax)
 fig
-# histogram(sim, normalize = true, label = "Histogram")
-# plot!(x, [target envelop], width = 2, label = ["Normal(μ, σ)" "Envelop"])
 ```
 
 
@@ -80,25 +79,16 @@ support = (0.0, Inf)
 
 obj = Objective(x -> log(f(x)))
 sam = ARSampler(obj, support)
-@time sim = sample!(sam, 10000, max_segments = 5)
+sim = sample!(sam, 10000, max_segments = 5)
 
 # Verify result
-x = range(0.0, 8.0, length=100)
-envelop = eval_hull.(sam.upper_hull, x)
-target = f.(x)
 mx = maximum(sim)
-fig, ax, p = hist(sim, bins=50, normalization = :pdf, label = "Samples");
-lines!(ax, 0..mx, y -> f(y), label = "Target", color = :orange)
-lines!(ax, 0..mx, y -> exp(eval_hull(sam.upper_hull, y)), label = "Envelop", color = :black, alpha = 0.8)
-absc = abscissae(sam.upper_hull)
-scatter!(ax, absc, exp.(eval_hull.(sam.upper_hull, absc)), label = "Abscissae", color = :red)
+fig, ax, p = hist(sim, bins=50, color = :grey, normalization = :pdf, label = "Samples");
+hullplot!(ax, 0..mx, sam, target = true)
 axislegend(ax)
 fig
 ```
 
-      0.000772 seconds (100 allocations: 85.211 KiB)
-
-
 ![](img/example2.svg)
 
 ### Truncated distributions and unknown normalization constant
@@ -111,23 +101,18 @@ We don't to provide an exact density--it will sample up to proportionality--and
 f(x) = β^α * x^(α-1) * exp(-β*x) / gamma(α)
 support = (1.0, 3.5)
 
-# Build the sampler and simulate 10,000 samples
-sampler = RejectionSampler(f, support)
-@time sim = run_sampler!(sampler, 10000) 
+obj = Objective(x -> log(f(x)))
+sam = ARSampler(obj, support)
+sim = sample!(sam, 10000, max_segments = 10)
 
 # Plot the results and compare to target distribution
-x = range(0.01, 8.0, length=100)
-envelop = [eval_envelop(sampler.envelop, xi) for xi in x]
-target = [f(xi) for xi in x]
-
-histogram(sim, normalize = true, label = "histogram")
-plot!(x, [target envelop], width = 2, label = ["target density" "envelop"])
+fig, ax, p = hist(sim, bins=50, color = :grey, normalization = :pdf, label = "Samples");
+hullplot!(ax, 0.01..8.0, sam, target = true)
+axislegend(ax)
+fig
 ```
 
-      0.007766 seconds (181.82 k allocations: 3.024 MiB)
-
-
-![](img/example3.png)
+![](img/example3.svg)
 
 ### Elastic Net Distribution
 
@@ -140,40 +125,22 @@ function f(x, μ, λ1, λ2)
       nl = λ1 * abs(δ) + λ2 * δ^2
       return exp(-nl)
 end
-support = (-Inf, Inf)
 
-# Build the sampler and simulate 10,000 samples
-μ, λ1, λ2 = 0.0, 2.0, 1.0
-sampler = RejectionSampler(x -> f(x, μ, λ1, λ2), support, max_segments = 5)
-@time sim = run_sampler!(sampler, 10000);
-
-# Plot the results and compare to target distribution
-x = range(-2.3, 2.3, length=100)
-envelop = [eval_envelop(sampler.envelop, xi) for xi in x]
-target = [f(xi, μ, λ1, λ2) for xi in x]
+support = (-Inf, Inf)
+mu, L1, L2 = 0.0, 2.0, 1.0
+obj = Objective(x -> log(f(x, mu, L1, L2)))
+sam = ARSampler(obj, support)
+sim = sample!(sam, 10000, max_segments = 10)
 
-histogram(sim, normalize = true, label = "histogram")
-plot!(x, [target envelop], width = 2, label = ["target density" "envelop"])
+fig, ax, p = hist(sim, bins=50, color = :grey, normalization = :pdf, label = "Samples");
+hullplot!(ax, -3..3, sam, target = true)
+axislegend(ax)
+fig
 ```
 
-![](img/example4.png)
-
-### Tips for numerical stability, use of logdensity
-
-Here are some tips:
+![](img/example4.svg)
 
-- Make sure the logdensity is numerically stable in the domain and avoid logdensity values > 25 (since
-  the evaluation of the envelop requires exponentials);
-- Use log densities instead of densities using the keyword `logdensity=true`;
-- Specify a `min_slope` and `max_slope` to find better initial points. The default is 1e-6 and 1e6, respectively.
-  The `min_slope` is the minimum slope of the logdensity in the initial points  of the envelop in absolute value. In general,
-  it is a good idea to leave `min_slope` with the default and try `max_slope=10.0` or a smaller number.
-- Try setting `δ` to a smaller value in the search_grid. The default is 0.5. 
-
-
-⚠️ *Warning* ⚠️: Using `logdensity=true` will be the default in v1.0.
-
-Here is an example
+#### Example of more complicated density
 
 ```julia
 import StatsFuns: logsumexp
@@ -185,36 +152,28 @@ theta = 1.0
 
 # a complicated logdensity
 logf(v) = n * v - (n - k * alpha) * logsumexp([v, log(tau)]) - theta / alpha * ( (tau + exp(v) )^alpha )
+f(x) = exp(logf(x))
 
 # run sampler
 δ = 0.1
 support = (-Inf, Inf)
 search = (0.0, 10.0)
-sampler = RejectionSampler(logf, support, δ, max_segments=10, logdensity=true, search_range=search, max_slope=10.0)
-@time sim = run_sampler!(sampler, 10000)
-```
+obj = Objective(logf)
+sam = ARSampler(obj, support, search)
+sim = sample!(sam, 10000, max_segments = 10)
 
-```
-[ Info: initial points found at 1.08, 5.43 with grads 9.94522619043481, -9.98968199019509
-      0.016296 seconds (371.21 k allocations: 6.850 MiB)
-```
-
-
-```julia
 x = range(0, 10, length=200)
 normconst = sum(f.(x)) * (x[2] - x[1])
-envelop = [eval_envelop(sampler.envelop, xi) for xi in x] ./ normconst
-target = [f(xi) for xi in x] ./ normconst
-
-# make two plots of logf and f
-p1 = plot(logf, -20, 20, label = "logf")
-p2 = histogram(sim, normalize=true, label="histogram")
-plot!(p2, x, [target envelop], width=2, label=["target density" "envelop"])
 
-plot(p1, p2, layout = (1, 2))
+fig, ax, p = lines(-20..20, logf);
+ax2 = Axis(fig[1,2])
+hist!(ax2, sim, bins=50, color = :grey, normalization = :pdf, label = "Samples")
+hullplot!(ax2, 0..10, sam, target = true, normconst = 1 / normconst)
+axislegend(ax2)
+fig
 ```
 
-![](img/example5.png)
+![](img/example5.svg)
 
 ## Citation
 
@@ -223,7 +182,7 @@ plot(p1, p2, layout = (1, 2))
 ```bibtex
 @manual{tec2018ars,
   title = {AdaptiveRejectionSampling.jl},
-  author = {Mauricio Tec},
+  author = {Mauricio Tec, Elias Sjölin},
   year = {2018},
   url = {https://github.com/mauriciogtec/AdaptiveRejectionSampling.jl}
 }