Added test for Heteroscedastic Likelihood

theogf · Jul 1, 2019 · 106aec9 · 106aec9
1 parent 76793b5
commit 106aec9
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Showing 3 changed files with 7 additions and 5 deletions.
diff --git a/src/likelihood/gaussian.jl b/src/likelihood/gaussian.jl
@@ -40,7 +40,7 @@ function Base.show(io::IO,model::GaussianLikelihood{T}) where T
     print(io,"Gaussian likelihood")
 end
 
-function init_likelihood(likelihood::GaussianLikelihood{T},inference::Inference{T},nLatent::Integer,nSamplesUsed::Integer) where {T<:Real}
+function init_likelihood(likelihood::GaussianLikelihood{T},inference::Inference{T},nLatent::Int,nSamplesUsed::Int,nFeatures::Int) where {T<:Real}
     if length(likelihood.ϵ) ==1 && length(likelihood.ϵ) != nLatent
         return GaussianLikelihood{T}([likelihood.ϵ[1] for _ in 1:nLatent],[fill(inv(likelihood.ϵ[1]),nSamplesUsed) for _ in 1:nLatent])
     elseif length(likelihood.ϵ) != nLatent
@@ -62,7 +62,7 @@ function local_updates!(model::SVGP{GaussianLikelihood{T}}) where {T<:Real}
         #TODO make it a moving average
         ρ = inv(sqrt(1+model.inference.nIter))
         model.likelihood.ϵ .= (1-ρ)*model.likelihood.ϵ + ρ/model.inference.nSamplesUsed *broadcast((y,κ,μ,Σ,K̃)->sum(abs2.(y[model.inference.MBIndices]-κ*μ))+opt_trace(κ*Σ,κ)+sum(K̃),model.y,model.κ,model.μ,model.Σ,model.K̃)
-        @show model.likelihood.ϵ
+        model.likelihood.ϵ
     else
         model.likelihood.ϵ .= 1.0/model.inference.nSamplesUsed *broadcast((y,κ,μ,Σ,K̃)->sum(abs2.(y-κ*μ))+opt_trace(κ*Σ,κ)+sum(K̃),model.y,model.κ,model.μ,model.Σ,model.K̃)
     end

diff --git a/test/test_VGP.jl b/test/test_VGP.jl
@@ -19,7 +19,7 @@ width = maximum(f)-minimum(f)
 normf = (f.-minimum(f))/width*K
 
 y = Dict("Regression"=>f,"Classification"=>sign.(f),"MultiClass"=>floor.(Int64,normf),"Event"=>rand.(Poisson.(2.0*AGP.logistic.(f))))
-reg_likelihood = ["GaussianLikelihood","StudentTLikelihood","LaplaceLikelihood"]
+reg_likelihood = ["GaussianLikelihood","StudentTLikelihood","LaplaceLikelihood","HeteroscedasticLikelihood"]
 class_likelihood = ["BayesianSVM","LogisticLikelihood"]
 multiclass_likelihood = ["LogisticSoftMaxLikelihood","SoftMaxLikelihood"]
 event_likelihood = ["PoissonLikelihood"]

diff --git a/test/testingtools.jl b/test/testingtools.jl
@@ -2,7 +2,8 @@ methods_implemented = Dict{String,Vector{String}}()
 methods_implemented["GaussianLikelihood"] = []
 methods_implemented["StudentTLikelihood"] = ["AnalyticVI","AnalyticSVI"] # ["QuadratureVI","QuadratureSVI"]
 methods_implemented["LaplaceLikelihood"] = ["AnalyticVI","AnalyticSVI"]
-methods_implemented["LogisticLikelihood"] = ["AnalyticVI","AnalyticSVI"]# ["NumericalVI","NumericalSVI"]
+methods_implemented["HeteroscedasticLikelihood"] = []
+methods_implemented["LogisticLikelihood"] = ["AnalyticVI","AnalyticSVI"]
 methods_implemented["BayesianSVM"] = ["AnalyticVI","AnalyticSVI"]
 methods_implemented["LogisticSoftMaxLikelihood"] = ["AnalyticVI","AnalyticSVI"]# "NumericalVI","NumericalSVI"]
 methods_implemented["SoftMaxLikelihood"] = ["QuadratureVI","QuadratureSVI"]
@@ -12,6 +13,7 @@ methods_implemented_VGP = deepcopy(methods_implemented)
 push!(methods_implemented_VGP["StudentTLikelihood"],"GibbsSampling")
 push!(methods_implemented_VGP["LogisticLikelihood"],"GibbsSampling")
 push!(methods_implemented_VGP["LogisticSoftMaxLikelihood"],"GibbsSampling")
+push!(methods_implemented_VGP["HeteroscedasticLikelihood"],"AnalyticVI")
 methods_implemented_SVGP = deepcopy(methods_implemented)
 methods_implemented_SVGP["GaussianLikelihood"] = ["AnalyticVI","AnalyticSVI"]
 
@@ -32,7 +34,7 @@ function testconv(model::AbstractGP,problem_type::String,X::AbstractArray,y::Abs
     py_pred = proba_y(model,X)
     if problem_type == "Regression"
         @show err = mean(abs.(y_pred-y))
-        return err < 0.5
+        return err < 0.8
     elseif problem_type == "Classification"
         @show err = mean(y_pred.!=y)
         return err < 0.5