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Raphael Saavedra committed Nov 27, 2022
2 parents 91b4ce3 + 04456d6 commit feb92dc
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Showing 8 changed files with 42 additions and 40 deletions.
4 changes: 2 additions & 2 deletions .github/workflows/ci.yml
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Expand Up @@ -15,10 +15,10 @@ jobs:
- version: '1'
os: ubuntu-latest
arch: x64
- version: '1.0'
- version: '1.6'
os: ubuntu-latest
arch: x64
- version: '1.0'
- version: '1.6'
os: ubuntu-latest
arch: x86
steps:
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4 changes: 2 additions & 2 deletions .github/workflows/documentation.yml
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Expand Up @@ -12,8 +12,8 @@ jobs:
- uses: actions/checkout@v2
- uses: julia-actions/setup-julia@latest
with:
# Build documentation on Julia 1.0
version: '1.0'
# Build documentation on Julia 1.6
version: '1.6'
- name: Install dependencies
run: julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
- name: Build and deploy
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2 changes: 1 addition & 1 deletion Project.toml
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Expand Up @@ -14,7 +14,7 @@ SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"

[compat]
Distributions = "0.23, 0.24, 0.25"
Distributions = "0.25"
HypothesisTests = "0.10"
Optim = "1.2"
RecipesBase = "1"
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11 changes: 9 additions & 2 deletions src/ScoreDrivenModels.jl
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@@ -1,7 +1,14 @@
module ScoreDrivenModels

using Distributions, Optim, SpecialFunctions, HypothesisTests, RecipesBase, StatsBase
using LinearAlgebra, Printf
using Distributions
using Distributions: AffineDistribution, value_support
using HypothesisTests
using LinearAlgebra
using Optim
using Printf
using RecipesBase
using SpecialFunctions
using StatsBase

import Base: length, deepcopy, show

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27 changes: 11 additions & 16 deletions src/distributions/betafour.jl
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Expand Up @@ -6,7 +6,7 @@
* `time_varying_params` map.
* Default link
Right now when estimating a BetaFourParameters model is recomended to provide fixed parameters a and c, dont
Right now when estimating a BetaFourParameters model is recomended to provide fixed parameters a and c, dont
with the code:
```julia
Expand All @@ -15,49 +15,49 @@ gas_beta_4.ω[1] = minimum(y) - 0.1*std(y) # parameter a
gas_beta_4.ω[2] = maximum(y) + 0.1*std(y) # parameter c
```
This code is a simple and not very accurate heuristic on how to estimate the a and c parameters. If you have estimated
This code is a simple and not very accurate heuristic on how to estimate the a and c parameters. If you have estimated
using maximum likelihood it will be probably better
The fact occurs because if in the optimization the gradient makes `c < maximum(y)` or `a > minimum(y)` then it
The fact occurs because if in the optimization the gradient makes `c < maximum(y)` or `a > minimum(y)` then it
is impossible that y comes from the BetaFourParameters distribution leading to DomainErrors
"""
BetaFourParameters

function score!(score_til::Matrix{T}, y::T, ::Type{BetaFourParameters}, param::Matrix{T}, t::Int) where T
score_til[t, 1] = -(param[t, 3] - 1)/(y - param[t, 1]) + (param[t, 4] + param[t, 3] - 1)/(param[t, 2] - param[t, 1])
score_til[t, 2] = (param[t, 4] - 1)/(param[t, 2] - y) - (param[t, 4] + param[t, 3] - 1)/(param[t, 2] - param[t, 1])
score_til[t, 3] = log(y - param[t, 1]) - log(param[t, 2] - param[t, 1]) +
score_til[t, 3] = log(y - param[t, 1]) - log(param[t, 2] - param[t, 1]) +
digamma(param[t, 3] + param[t, 4]) - digamma(param[t, 3])
score_til[t, 4] = log(param[t, 2] - y) - log(param[t, 2] - param[t, 1]) +
score_til[t, 4] = log(param[t, 2] - y) - log(param[t, 2] - param[t, 1]) +
digamma(param[t, 3] + param[t, 4]) - digamma(param[t, 4])
return
end

function log_likelihood(::Type{BetaFourParameters}, y::Vector{T}, param::Matrix{T}, n::Int) where T
loglik = 0.0
for t in 1:n
loglik += (param[t, 3] - 1)*log(y[t] - param[t, 1]) + (param[t, 4] - 1) * log(param[t, 2] - y[t]) -
loglik += (param[t, 3] - 1)*log(y[t] - param[t, 1]) + (param[t, 4] - 1) * log(param[t, 2] - y[t]) -
(param[t, 3] + param[t, 4] - 1) * log(param[t, 2] - param[t, 1]) - logbeta(param[t, 3], param[t, 4])
end
return -loglik
end

# Links
function link!(param_tilde::Matrix{T}, ::Type{BetaFourParameters}, param::Matrix{T}, t::Int) where T
function link!(param_tilde::Matrix{T}, ::Type{BetaFourParameters}, param::Matrix{T}, t::Int) where T
param_tilde[t, 1] = link(IdentityLink, param[t, 1])
param_tilde[t, 2] = link(IdentityLink, param[t, 2])
param_tilde[t, 3] = link(LogLink, param[t, 3], zero(T))
param_tilde[t, 4] = link(LogLink, param[t, 4], zero(T))
return
end
function unlink!(param::Matrix{T}, ::Type{BetaFourParameters}, param_tilde::Matrix{T}, t::Int) where T
function unlink!(param::Matrix{T}, ::Type{BetaFourParameters}, param_tilde::Matrix{T}, t::Int) where T
param[t, 1] = unlink(IdentityLink, param_tilde[t, 1])
param[t, 2] = unlink(IdentityLink, param_tilde[t, 2])
param[t, 3] = unlink(LogLink, param_tilde[t, 3], zero(T))
param[t, 4] = unlink(LogLink, param_tilde[t, 4], zero(T))
return
end
function jacobian_link!(aux::AuxiliaryLinAlg{T}, ::Type{BetaFourParameters}, param::Matrix{T}, t::Int) where T
function jacobian_link!(aux::AuxiliaryLinAlg{T}, ::Type{BetaFourParameters}, param::Matrix{T}, t::Int) where T
aux.jac[1] = jacobian_link(IdentityLink, param[t, 1])
aux.jac[2] = jacobian_link(IdentityLink, param[t, 2])
aux.jac[3] = jacobian_link(LogLink, param[t, 3], zero(T))
Expand All @@ -69,8 +69,8 @@ end
function update_dist(::Type{BetaFourParameters}, param::Matrix{T}, t::Int) where T
small_threshold!(param[:, 3:4], SMALL_NUM, t)
beta = Beta(param[t, 3], param[t, 4])
return LocationScale(param[t, 1], param[t, 2] - param[t, 1], beta)
end
return AffineDistribution(param[t, 1], param[t, 2] - param[t, 1], beta)
end

function params_sdm(d::BetaFourParameters)
return (d.μ, d.σ + d.μ, Distributions.params(d.ρ)...)
Expand All @@ -79,8 +79,3 @@ end
function num_params(::Type{BetaFourParameters})
return 4
end

# d = Beta(1, 1)
# d2 = LocationScale(-1, 3, d)
# maximum(d2)
# minimum(d2)
4 changes: 2 additions & 2 deletions src/distributions/non_native_dists.jl
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@@ -1,6 +1,6 @@
# Define TDistLocationScale as the location scale transformation of the Distributions.jl TDist
export TDistLocationScale
TDistLocationScale = LocationScale{Float64,TDist{Float64}}
TDistLocationScale = AffineDistribution{Float64,value_support(TDist),TDist{Float64}}

export BetaFourParameters
BetaFourParameters = LocationScale{Float64,Beta{Float64}}
BetaFourParameters = AffineDistribution{Float64,value_support(Beta),Beta{Float64}}
20 changes: 10 additions & 10 deletions src/distributions/tdistlocationscale.jl
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Expand Up @@ -16,11 +16,11 @@ TDistLocationScale

function score!(score_til::Matrix{T}, y::T, ::Type{TDistLocationScale}, param::Matrix{T}, t::Int) where T
score_til[t, 1] = ((param[t, 3] + 1) * (y - param[t, 1])) / ((y - param[t, 1])^2 + param[t, 2] * param[t, 3])
score_til[t, 2] = -(param[t, 3] * (param[t, 2] - (y - param[t, 1])^2)) / (2 * param[t, 2] * (param[t, 3] * param[t, 2] +
score_til[t, 2] = -(param[t, 3] * (param[t, 2] - (y - param[t, 1])^2)) / (2 * param[t, 2] * (param[t, 3] * param[t, 2] +
(y - param[t, 1])^2))
score_til[t, 3] = ((((y - param[t, 1])^2)*(param[t, 3] + 1)/(param[t, 3] * (y - param[t, 1])^2 + param[t, 2] * param[t, 3]^2)) -
1/param[t, 3] -
log(((y - param[t, 1])^2)/(param[t, 2] * param[t, 3]) + 1) -
1/param[t, 3] -
log(((y - param[t, 1])^2)/(param[t, 2] * param[t, 3]) + 1) -
digamma(param[t, 3] / 2) + digamma((param[t, 3] + 1) / 2)) / 2
return
end
Expand Down Expand Up @@ -49,35 +49,35 @@ function log_likelihood(::Type{TDistLocationScale}, y::Vector{T}, param::Matrix{
end

# Links
function link!(param_tilde::Matrix{T}, ::Type{TDistLocationScale}, param::Matrix{T}, t::Int) where T
function link!(param_tilde::Matrix{T}, ::Type{TDistLocationScale}, param::Matrix{T}, t::Int) where T
param_tilde[t, 1] = link(IdentityLink, param[t, 1])
param_tilde[t, 2] = link(LogLink, param[t, 2], zero(T))
param_tilde[t, 3] = link(LogLink, param[t, 3], zero(T))
return
end
function unlink!(param::Matrix{T}, ::Type{TDistLocationScale}, param_tilde::Matrix{T}, t::Int) where T
function unlink!(param::Matrix{T}, ::Type{TDistLocationScale}, param_tilde::Matrix{T}, t::Int) where T
param[t, 1] = unlink(IdentityLink, param_tilde[t, 1])
param[t, 2] = unlink(LogLink, param_tilde[t, 2], zero(T))
param[t, 3] = unlink(LogLink, param_tilde[t, 3], zero(T))
return
end
function jacobian_link!(aux::AuxiliaryLinAlg{T}, ::Type{TDistLocationScale}, param::Matrix{T}, t::Int) where T
function jacobian_link!(aux::AuxiliaryLinAlg{T}, ::Type{TDistLocationScale}, param::Matrix{T}, t::Int) where T
aux.jac[1] = jacobian_link(IdentityLink, param[t, 1])
aux.jac[2] = jacobian_link(LogLink, param[t, 2], zero(T))
aux.jac[3] = jacobian_link(LogLink, param[t, 3], zero(T))
return
end

# utils
# utils
function update_dist(::Type{TDistLocationScale}, param::Matrix{T}, t::Int) where T
tdist = TDist(param[t, 3])
return LocationScale(param[t, 1], sqrt(param[t, 2]), tdist)
end
return AffineDistribution(param[t, 1], sqrt(param[t, 2]), tdist)
end

function params_sdm(d::TDistLocationScale)
return (d.μ, d.σ^2, Distributions.params(d.ρ)...)
end

function num_params(::Type{TDistLocationScale})
return 3
end
end
10 changes: 5 additions & 5 deletions test/utils.jl
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Expand Up @@ -20,8 +20,8 @@ function instantiate_dist(D::Type{<:Distribution})
end
end

function test_score_mean(D::Type{<:Distribution}; n::Int = 10^7, seed::Int = 10,
atol::Float64 = 1e-3, rtol::Float64 = 1e-3)
function test_score_mean(D::Type{<:Distribution}; n::Int = 10^6, seed::Int = 10,
atol::Float64 = 1e-2, rtol::Float64 = 1e-2)
Random.seed!(seed)
dist = instantiate_dist(D)
pars = permutedims([ScoreDrivenModels.params_sdm(dist)...])
Expand Down Expand Up @@ -55,7 +55,7 @@ function test_fisher_information(D::Type{<:Distribution}; n::Int = 10^6, seed::I

# Some distributions might not have the fisher information yet available
if D in FI_NOT_IMPLEMENTED
return
return
else
ScoreDrivenModels.fisher_information!(aux_lin_alg, D, pars, 1)
end
Expand Down Expand Up @@ -94,10 +94,10 @@ end

function test_dist_utils(D::Type{<:Distribution})
# num_params
dist = instantiate_dist(D)
dist = instantiate_dist(D)
n_pars = ScoreDrivenModels.num_params(D)
@test n_pars == length(ScoreDrivenModels.params_sdm(dist))

# update_dist
t = 1
pars = permutedims([ScoreDrivenModels.params_sdm(dist)...])
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2 comments on commit feb92dc

@raphaelsaavedra
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Registration pull request created: JuliaRegistries/General/72949

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.1.11 -m "<description of version>" feb92dc11294b7ff16f725778d1f7d050ee25292
git push origin v0.1.11

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