Add of distributed algorithm for the reference table creation in abc model...

Add of distributed algorithm for the reference table creation in abc model choice. Documentation for abc model choice methods.

core/MarkovProcesses.jl

@@ -17,6 +17,7 @@ import Random: rand, rand!
 import ScikitLearn
 import ScikitLearn: fit!, predict, get_params
 import ScikitLearn.GridSearch: GridSearchCV
+import SharedArrays: SharedVector, SharedMatrix, sdata
 import StaticArrays: SVector, @SVector
 # Python objects import
 import PyCall: PyNULL

tests/abc_model_choice/toy_example.jl

 
-using SpecialFunctions
-using LinearAlgebra
-using Random
-using Distributions
-using MarkovProcesses
+@everywhere begin
+    using Distributions
+    using MarkovProcesses
+    using SpecialFunctions
+    using LinearAlgebra
+    using Random
+end
 using ScikitLearn
 @sk_import metrics: (accuracy_score, classification_report)
 
-global n = 20
+@everywhere begin
+    global n = 20
 
-struct Model1 <: Model end
-struct Model2 <: Model end
-struct Model3 <: Model end
-import MarkovProcesses: simulate
+    struct Model1 <: Model end
+    struct Model2 <: Model end
+    struct Model3 <: Model end
+    import MarkovProcesses: simulate
 
-function simulate(m::Model1)
-    param = rand(Exponential(1))
-    return rand(Exponential(param), n)
-end
-function simulate(m::Model2)
-    param = rand(Normal())
-    return rand(LogNormal(param,1), n)
-end
-function simulate(m::Model3)
-    param = rand(Exponential(1))
-    return rand(Gamma(2,1/param), n)
-end
+    function simulate(m::Model1)
+        param = rand(Exponential(1))
+        return rand(Exponential(param), n)
+    end
+    function simulate(m::Model2)
+        param = rand(Normal())
+        return rand(LogNormal(param,1), n)
+    end
+    function simulate(m::Model3)
+        param = rand(Exponential(1))
+        return rand(Gamma(2,1/param), n)
+    end
 
-m1, m2, m3 = Model1(), Model2(), Model3()
-lh_m1(s) = exp(log(gamma(n+1)) - (n+1)*log(1+s[1]))
-lh_m2(s) = exp(-s[2]^2/(2n*(n+1)) - (s[3]^2)/2 + (s[2]^2)/(2n) - s[2]) * (2pi)^(-n/2)*(n+1)^(-1/2)
-lh_m3(s) = exp(s[2])*gamma(2n+1)/gamma(2)^n * (1+s[1])^(-2n-1)
+    lh_m1(s) = exp(log(gamma(n+1)) - (n+1)*log(1+s[1]))
+    lh_m2(s) = exp(-s[2]^2/(2n*(n+1)) - (s[3]^2)/2 + (s[2]^2)/(2n) - s[2]) * (2pi)^(-n/2)*(n+1)^(-1/2)
+    lh_m3(s) = exp(s[2])*gamma(2n+1)/gamma(2)^n * (1+s[1])^(-2n-1)
 
-ss_func(y) = [sum(y), sum(log.(y)), sum(log.(y).^2)]
-dist_l2(s_sim,s_obs) = norm(s_sim-s_obs)
+    ss_func(y) = [sum(y), sum(log.(y)), sum(log.(y).^2)]
+    dist_l2(s_sim,s_obs) = norm(s_sim-s_obs)
+end
 
+m1, m2, m3 = Model1(), Model2(), Model3()
 observations = simulate(m3)
 ss_observations = ss_func(observations)
 models = [m1, m2, m3]
-abc_testset = abc_model_choice_dataset(models, ss_observations, ss_func, dist_l2, 1000, 1000)
+println("Testset 10000 samples")
+@timev abc_testset = abc_model_choice_dataset(models, ss_observations, ss_func, dist_l2, 10000, 10000; dir_results = "toy_ex")
 
 list_lh = [lh_m1, lh_m2, lh_m3]
 prob_model(ss, list_lh, idx_model) = list_lh[idx_model](ss) / sum([list_lh[i](ss) for i = eachindex(list_lh)])
@@ -65,9 +70,12 @@ savefig("set.svg")
 =#
 
 grid = Dict(:n_estimators => [500], :min_samples_leaf => [1], :min_samples_split => [2], :n_jobs => [8])
+println("RF ABC")
 @timev res_rf_abc = rf_abc_model_choice(models, ss_observations, ss_func, 29000; hyperparameters_range = grid)
 @show posterior_proba_model(res_rf_abc)
 X_testset = transpose(abc_testset.X)
 println(classification_report(y_true = abc_testset.y, y_pred = predict(res_rf_abc.clf, X_testset)))
 @show accuracy_score(abc_testset.y, predict(res_rf_abc.clf, X_testset))
 
+return true
+

tests/abc_model_choice/toy_example_ma.jl

@@ -80,3 +80,5 @@ X_testset = transpose(abc_testset.X)
 println(classification_report(y_true = abc_testset.y, y_pred = predict(res_rf_abc.clf, X_testset)))
 @show accuracy_score(abc_testset.y, predict(res_rf_abc.clf, X_testset))
 
+return true
+

tests/run_all.jl

@@ -5,4 +5,5 @@ include("run_dist_lp.jl")
 include("run_automata.jl")
 include("run_cosmos.jl")
 include("run_abc_smc.jl")
+include("run_abc_model_choice.jl")