3: Add function fit_beta_mixture

.gitignore

@@ -22,3 +22,7 @@ docs/site/
 # committed for packages, but should be committed for applications that require a static
 # environment.
 Manifest.toml
+
+.vscode/settings.json
+
+.DS_Store

Project.toml

@@ -9,7 +9,11 @@ DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 ExpectationMaximization = "e1fe09cc-5134-44c2-a941-50f4cd97986a"
 FreqTables = "da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Pluto = "c3e4b0f8-55cb-11ea-2926-15256bba5781"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SpecialFunctions = "276daf66-3868-5448-9aa4-cd146d93841b"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Turing = "fce5fe82-541a-59a6-adf8-730c64b5f9a0"

design/ReconcileWithR.jl

@@ -684,7 +684,7 @@ weakdeps = ["Dates", "LinearAlgebra"]
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.0.5+0"
+version = "1.0.2+0"
 
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -1623,7 +1623,7 @@ version = "0.42.2+0"
 [[deps.Pkg]]
 deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
 uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
-version = "1.9.2"
+version = "1.9.0"
 
 [[deps.PlotThemes]]
 deps = ["PlotUtils", "Statistics"]

src/SafetySignalDetection.jl

@@ -3,10 +3,17 @@ module SafetySignalDetection
 using Turing
 using StatsPlots
 using Distributions
+using SpecialFunctions
+using Statistics
+using LinearAlgebra
+using ExpectationMaximization
 
 export 
-    meta_analytic
+    meta_analytic,
+    fit_beta_mixture
 
 include("meta_analytic.jl")
+include("fit_mle.jl")
+include("fit_beta_mixture.jl")
 
 end 

src/fit_beta_mixture.jl

@@ -0,0 +1,26 @@
+function init_beta_dists(n_components::Int) 
+	zero_one_range = range(start = 0, stop = 1, length = n_components + 2)
+	alpha_range = zero_one_range[2:(n_components + 1)]
+	[Beta(alpha, 1) for alpha in alpha_range]
+end
+
+"""
+    Fit a beta mixture to a vector of prior samples
+
+This function returns a beta mixture of `n_components` components approximating
+the distribution of the sample vector `x`.
+"""
+function fit_beta_mixture(x::AbstractArray{T}, n_components::Int) where T<:Real
+    0 < n_components || throw(DomainError(n_components, "there must be at least one component"))
+    # Remove outliers to stabilize the fitting.
+	lower_quant = quantile!(x, 0.001)
+	upper_quant = quantile!(x, 0.999)
+	x = filter(y -> y > lower_quant && y < upper_quant, x)
+
+	# We initialize here with Beta distributions that are not identical but have increasing alpha parameters.
+	beta_dists = init_beta_dists(n_components)
+	mix_guess = MixtureModel(beta_dists)
+
+	# Fit the MLE with the classic EM algorithm.
+	fit_mle(mix_guess, x)
+end

src/fit_mle.jl

@@ -0,0 +1,87 @@
+import Distributions: fit_mle, suffstats, varm
+
+# Weighted MLE for beta distribution
+# This supplements Distributions.jl and is needed for the classic EM algorithm to work for the beta distribution.
+
+# sufficient statistics - these capture everything of the data we need
+struct BetaStats <: SufficientStats
+    sum_log_x::Float64 # (weighted) sum of log(x)
+    sum_log_1mx::Float64 # (weighted) sum of log(1 - x)
+    tw::Float64 # total sample weight
+    x_bar::Float64 # (weighted) mean of x
+    v_bar::Float64 # (weighted) variance of x
+end
+
+function suffstats(::Type{<:Beta}, x::AbstractArray{T}, w::AbstractArray{T}) where T<:Real
+
+    tw = 0.0
+    sum_log_x = 0.0 * zero(T)
+    sum_log_1mx = 0.0 * zero(T)
+    x_bar = 0.0 * zero(T)
+
+    for i in eachindex(x, w)
+        @inbounds xi = x[i]
+        0 < xi < 1 || throw(DomainError(xi, "samples must be larger than 0 and smaller than 1"))
+        @inbounds wi = w[i]
+        wi >= 0 || throw(DomainError(wi, "weights must be non-negative"))
+        isfinite(wi) || throw(DomainError(wi, "weights must be finite"))
+        @inbounds sum_log_x += wi * log(xi)
+        @inbounds sum_log_1mx += wi * log(one(T) - xi)
+        @inbounds x_bar += wi * xi
+        tw += wi
+    end
+    sum_log_x /= tw   
+    sum_log_1mx /= tw
+    x_bar /= tw
+    v_bar = varm(x, x_bar)
+
+    BetaStats(sum_log_x, sum_log_1mx, tw, x_bar, v_bar)
+end
+
+# without weights we just put weight 1 for each observation
+function suffstats(::Type{<:Beta}, x::AbstractArray{T}) where T<:Real
+
+    w = ones(Float64, length(x))
+    suffstats(Beta, x, w)
+
+end
+
+# generic fit function based on the sufficient statistics, on the log scale to be robust
+function fit_mle(::Type{<:Beta}, ss::BetaStats;
+    maxiter::Int=1000, tol::Float64=1e-14)
+
+    # Initialization of parameters at the moment estimators (I guess)
+    temp = ((ss.x_bar * (1 - ss.x_bar)) / ss.v_bar) - 1
+    α₀ = ss.x_bar * temp
+    β₀ = (1 - ss.x_bar) * temp
+
+    g₁ = ss.sum_log_x
+    g₂ = ss.sum_log_1mx
+
+    θ= [log(α₀) ; log(β₀)]
+
+    converged = false
+    t=0
+    while !converged && t < maxiter
+        t += 1
+        α = exp(θ[1])
+        β = exp(θ[2])
+        temp1 = digamma(α + β)
+        temp2 = trigamma(α + β)
+        temp3 = g₁ + temp1 - digamma(α)
+        grad = [temp3 * α
+                (temp1 + g₂ - digamma(β)) * β]
+        hess = [((temp2 - trigamma(α)) * α + temp3) * α             temp2 * β * α
+                temp2 * α * β       ((temp2 - trigamma(β)) * β + temp1 + g₂ - digamma(β)) * β  ]
+        Δθ = hess\grad #newton step
+        θ .-= Δθ
+        converged = dot(Δθ,Δθ) < 2*tol #stopping criterion
+    end
+
+    α = exp(θ[1])
+    β = exp(θ[2])
+    return Beta(α, β)
+end
+
+# then define methods for the original data
+fit_mle(::Type{<:Beta}, x::AbstractArray{T}, w::AbstractArray{T}; maxiter::Int=1000, tol::Float64=1e-14) where T<:Real = fit_mle(Beta, suffstats(Beta, x, w))

test/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"