Dev (#332)

* update Project.toml

* s/logpdf/logdensity/

* update deps

* scratchpad

* version bump

* representative => rootmeasure

* update dependency versions

* reduce dependencies

* update deps

* scratchpad

* representative => rootmeasure

* update dependency versions

* reduce dependencies

* `as` methods for `xform`

* cleanup

* require latest MeasureTheory

* dorp old distributions code

* drop old iid code

* drop extra space

* limit deps to three newest releases

* update dynamichmc

* add Aqua

* bump version

* Better `predict` method

* withmeasures(::ConditionalModel)

* update dependencies

* updating symbolics

* some updates to symbolics

* hmm example

* update MeasureBase bound

* better dispatch for `predict`

* drop redundant method

* remove whitespace

* bump version

* update for upcoming MeasureTheory release

* small change to toposort

* have `iid` use powermeasure

* tests passing!

* iid(n::Integer...)

* update dependencies

* start logdensity with partialstatic

* update test to account for partialstatic

* add `insupport`

* clean up insupport

* speed up model building

* updates

* starting updates for new MeasureTheory stuff

* get tests passing

* Kleisli => TransitionKernel

* bump MB dependency version

* Kleisli => TransitionKernel

* tests passing

* update dependencies

* update CI

* Should only need BayesianLinearRegression for testing

* bump version

Author: cscherrer

.github/workflows/ci.yml

@@ -17,7 +17,8 @@ jobs:
       fail-fast: false
       matrix:
         version:
-          - '1.5'
+          - '1.6'
+          - '1.7'
           - '1'
         os:
           - ubuntu-latest

Project.toml

@@ -1,15 +1,16 @@
 name = "Soss"
 uuid = "8ce77f84-9b61-11e8-39ff-d17a774bf41c"
 author = ["Chad Scherrer <[email protected]>"]
-version = "0.20.9"
+version = "0.21.0"
 
 [deps]
 ArrayInterface = "4fba245c-0d91-5ea0-9b3e-6abc04ee57a9"
+DensityInterface = "b429d917-457f-4dbc-8f4c-0cc954292b1d"
 DiffResults = "163ba53b-c6d8-5494-b064-1a9d43ac40c5"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 FillArrays = "1a297f60-69ca-5386-bcde-b61e274b549b"
 GeneralizedGenerated = "6b9d7cbe-bcb9-11e9-073f-15a7a543e2eb"
-IRTools = "7869d1d1-7146-5819-86e3-90919afe41df"
+IfElse = "615f187c-cbe4-4ef1-ba3b-2fcf58d6d173"
 JuliaVariables = "b14d175d-62b4-44ba-8fb7-3064adc8c3ec"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MLStyle = "d8e11817-5142-5d16-987a-aa16d5891078"
@@ -30,45 +31,46 @@ SimpleGraphs = "55797a34-41de-5266-9ec1-32ac4eb504d3"
 SimplePartitions = "ec83eff0-a5b5-5643-ae32-5cbf6eedec9d"
 SimplePosets = "b2aef97b-4721-5af9-b440-0bad754dc5ba"
 SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+Static = "aedffcd0-7271-4cad-89d0-dc628f76c6d3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-SymbolicCodegen = "fc9b0551-4f1b-42e1-8440-e8a535f5a551"
 SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
 TransformVariables = "84d833dd-6860-57f9-a1a7-6da5db126cff"
 TupleVectors = "615932cf-77b6-4358-adcd-5b7eba981d7e"
 
 [compat]
-ArrayInterface = "3"
+ArrayInterface = "5,6"
+DensityInterface = "0.4"
 DiffResults = "1"
 Distributions = "0.23, 0.24, 0.25"
-FillArrays = "0.10, 0.11, 0.12"
+FillArrays = "0.11, 0.12, 0.13"
 GeneralizedGenerated = "0.3"
-IRTools = "0.4"
+IfElse = "0.1"
 JuliaVariables = "0.2"
 MLStyle = "0.3,0.4"
 MacroTools = "0.5"
 MappedArrays = "0.3, 0.4"
-MeasureBase = "0.5"
-MeasureTheory = "0.13"
-NamedTupleTools = "0.12, 0.13"
-NestedTuples = "0.3"
-RecipesBase = "0.7,0.8, 1"
+MeasureBase = "0.9"
+MeasureTheory = "0.16"
+NamedTupleTools = "0.12, 0.13, 0.14"
+NestedTuples = "0.3.9"
+RecipesBase = "1"
 Reexport = "1"
 Requires = "1"
 RuntimeGeneratedFunctions = "0.5"
 SampleChains = "0.5"
 SimpleGraphs = "0.5, 0.6, 0.7"
 SimplePartitions = "0.2, 0.3"
-SimplePosets = "0.0, 0.1"
-SpecialFunctions = "0.9, 0.10, 1"
+SimplePosets = "0.1"
+SpecialFunctions = "1, 2"
+Static = "0.5, 0.6"
 StatsBase = "0.33"
-StatsFuns = "0.9"
-SymbolicCodegen = "0.2"
-SymbolicUtils = "0.15, 0.16, 0.17"
-TransformVariables = "0.4"
+StatsFuns = "0.9, 1"
+SymbolicUtils = "0.17, 0.18, 0.19"
+TransformVariables = "0.5, 0.6"
 TupleVectors = "0.1"
-julia = "1.5"
+julia = "1.6"
 
 [extras]
 Aqua = "4c88cf16-eb10-579e-8560-4a9242c79595"

demos/boundmodels.jl

@@ -10,7 +10,7 @@ m(μ=1.0)
 
 rand(m(μ=1.0))
 
-logdensity(m(μ=1.0),(x=0.4,))
+logdensityof(m(μ=1.0),(x=0.4,))
 # rand(m1)
 
 weightedSample(m(μ=1.0),(x=3,))

demos/hmm.jl

@@ -53,7 +53,7 @@ end
     end
 end
 
-# function Distributions.logdensity(c::Chain, xs)
+# function Distributions.logdensityof(c::Chain, xs)
 
 c = Chain(s0, step)
 

demos/repeated-measurements.jmd

@@ -92,8 +92,8 @@ for t in symlogdensity(m()).evalf(3).args
 
 <!--
 using BenchmarkTools
-@btime logdensity(m(),truth)
-@btime logdensity(m(),truth, codegen)
+@btime logdensityof(m(),truth)
+@btime logdensityof(m(),truth, codegen)
 
 f1 = Soss._codegen(m, true);
 f2 = Soss._codegen(m,false);
@@ -105,7 +105,7 @@ codegen(m(),truth)
 
 
 
-logdensity(m(), merge(truth, (p_bad=shuffle(truth.p_bad),)), codegen)
+logdensityof(m(), merge(truth, (p_bad=shuffle(truth.p_bad),)), codegen)
 
 
 @time result = dynamicHMC(m(), (y=truth.y,), codegen) ;

demos/repeated-measurements.md

@@ -192,8 +192,8 @@ _j1 = 1 _j2 = 1
 
 <!--
 using BenchmarkTools
-@btime logdensity(m(),truth)
-@btime logdensity(m(),truth, codegen)
+@btime logdensityof(m(),truth)
+@btime logdensityof(m(),truth, codegen)
 
 f1 = Soss._codegen(m, true);
 f2 = Soss._codegen(m,false);
@@ -205,7 +205,7 @@ codegen(m(),truth)
 
 
 
-logdensity(m(), merge(truth, (p_bad=shuffle(truth.p_bad),)), codegen)
+logdensityof(m(), merge(truth, (p_bad=shuffle(truth.p_bad),)), codegen)
 
 
 @time result = dynamicHMC(m(), (y=truth.y,), codegen) ;

docs/src/misc.md

@@ -65,7 +65,7 @@ function sourceWeightedSample(_data)
         proc(_m, st :: LineNumber) = nothing
 
         function proc(_m, st :: Sample)
-            st.x ∈ _datakeys && return :(_ℓ += logdensity($(st.rhs), $(st.x)))
+            st.x ∈ _datakeys && return :(_ℓ += logdensity_def($(st.rhs), $(st.x)))
             return :($(st.x) = rand($(st.rhs)))
         end
 

scratchpad/MCM.jl

@@ -7,7 +7,7 @@ function expect(x,ℓ)
 end
 
 x = Particles(5000,TDist(3))
-ℓ = logpdf(Normal(),x) - logpdf(TDist(3), x)
+ℓ = logdensityof(Normal(),x) - logdensityof(TDist(3), x)
 
 expect(x,ℓ)
 expect(x^2,ℓ)
@@ -26,7 +26,7 @@ expect(z^4,ℓ)
 # And now a new weighted variable
 
 y = Particles(5000,TDist(3))
-ℓ += logpdf(Normal(),y) - logpdf(TDist(3), y)
+ℓ += logdensityof(Normal(),y) - logdensityof(TDist(3), y)
 
 expect(y,ℓ)
 expect(y^2,ℓ)

scratchpad/SampleChainsDynamicHMC-demo.jl

@@ -44,8 +44,8 @@ plot(p, exp.([ℓ((p=pj,)) for pj in p]))
 
 using TransformVariables
 
-ℓ(x) = logdensity(Beta(4,3), x.p)
-t = xform(post)
+ℓ(x) = logdensity_def(Beta(4,3), x.p)
+t = as(post)
 chain = initialize!(DynamicHMCChain, ℓ, t)
 drawsamples!(chain, 10000)
 plot(ash(chain.p))

scratchpad/advancedhmc.jl

@@ -6,7 +6,7 @@ D = 10; initial_θ = rand(D)
 dist = Normal() ^ D
 
 # Define the target distribution
-ℓπ(θ) = logdensity(dist, θ)
+ℓπ(θ) = logdensity_def(dist, θ)
 
 # Set the number of samples to draw and warmup iterations
 n_samples, n_adapts = 2_000, 1_000