Speed up bootstrap

xKDR · Jan 20, 2023 · 4e5e50a · 4e5e50a
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Showing 5 changed files with 74 additions and 78 deletions.
diff --git a/src/bootstrap.jl b/src/bootstrap.jl
@@ -4,14 +4,16 @@ julia> using Random
 
 julia> apiclus1 = load_data("apiclus1");
 
+
 julia> clus_one_stage = SurveyDesign(apiclus1; clusters = :dnum, popsize=:fpc);
 
+
 julia> bootweights(clus_one_stage; replicates=1000, rng=MersenneTwister(111)) # choose a seed for deterministic results
 ReplicateDesign:
 data: 183×1044 DataFrame
 strata: none
 cluster: dnum
-    [637, 637, 637  …  448]
+    [61, 61, 61  …  815]
 popsize: [757, 757, 757  …  757]
 sampsize: [15, 15, 15  …  15]
 weights: [50.4667, 50.4667, 50.4667  …  50.4667]
@@ -20,32 +22,25 @@ replicates: 1000
 ```
 """
 function bootweights(design::SurveyDesign; replicates=4000, rng=MersenneTwister(1234))
-    H = length(unique(design.data[!, design.strata]))
     stratified = groupby(design.data, design.strata)
-    function replicate(stratified, H)
-        for h in 1:H
-            substrata = DataFrame(stratified[h])
-            psus = unique(substrata[!, design.cluster])
-            if length(psus) <= 1
-                stratified[h].whij .= 0 # hasn't been tested yet. 
+    H = length(keys(stratified))
+    substrata_dfs = []
+    for h in 1:H
+        substrata = DataFrame(stratified[h])
+        cluster_sorted = sort(substrata, design.cluster)
+        psus = unique(cluster_sorted[!, design.cluster])
+        npsus = [(count(==(i), cluster_sorted[!, design.cluster])) for i in psus]
+        nh = length(psus)
+        randinds = rand(rng, 1:(nh), replicates, (nh-1))
+        for replicate in 1:replicates
+            rh = zeros(Int, nh)
+            for i in randinds[replicate, :]
+                rh[i] += 1
             end
-            nh = length(psus)
-            randinds = rand(rng, 1:(nh), (nh-1)) # Main bootstrap algo. Draw nh-1 out of nh, with replacement.  
-            rh = [(count(==(i), randinds)) for i in 1:nh] # main bootstrap algo. 
-            gdf = groupby(substrata, design.cluster)
-            for i in 1:nh
-                gdf[i].whij = repeat([rh[i]], nrow(gdf[i])) .* gdf[i][!,design.weights] .* (nh / (nh - 1))
-            end            
-            stratified[h].whij = transform(gdf).whij
-
-        end
-        return transform(stratified, :whij)
+            cluster_sorted[!, "replicate_" * string(replicate)] = vcat([repeat([rh[i] * (nh / (nh-1))], npsus[i]) for i in 1:length(rh)]...) .* cluster_sorted[!, design.weights] 
+        end   
+        push!(substrata_dfs, cluster_sorted)
     end
-    df = replicate(stratified, H)
-    rename!(df, :whij => :replicate_1)
-    df.replicate_1 = disallowmissing(df.replicate_1)
-    for i in 2:(replicates)
-        df[!, "replicate_" * string(i)] = disallowmissing(replicate(stratified, H).whij)
-    end 
+    df = vcat(substrata_dfs...)
     return ReplicateDesign(df, design.cluster, design.popsize, design.sampsize, design.strata, design.weights, design.allprobs, design.pps, replicates) 
 end
diff --git a/src/mean.jl b/src/mean.jl
@@ -10,18 +10,18 @@ julia> clus_one_stage = SurveyDesign(apiclus1; clusters = :dnum, weights = :pw)
 
 julia> mean(:api00, clus_one_stage)
 1×2 DataFrame
- Row │ mean     SE      
-     │ Float64  Float64 
+ Row │ mean     SE
+     │ Float64  Float64
 ─────┼──────────────────
-   1 │ 644.169  23.2919
+   1 │ 644.169  23.2877
 
 julia> mean([:api00, :enroll], clus_one_stage)
 2×3 DataFrame
  Row │ names   mean     SE
      │ String  Float64  Float64
 ─────┼──────────────────────────
-   1 │ api00   644.169  23.2919
-   2 │ enroll  549.716  45.3655
+   1 │ api00   644.169  23.2877
+   2 │ enroll  549.716  46.2597
 ```
 """
 function mean(x::Symbol, design::ReplicateDesign)
@@ -52,17 +52,17 @@ julia> mean(:api00, :cname, clus_one_stage)
  Row │ cname        mean     SE
      │ String15     Float64  Any
 ─────┼───────────────────────────────────
-   1 │ Alameda      669.0    1.27388e-13
-   2 │ Fresno       472.0    1.13687e-13
-   3 │ Kern         452.5    0.0
-   4 │ Los Angeles  647.267  47.4938
-   5 │ Mendocino    623.25   1.0931e-13
-   6 │ Merced       519.25   4.57038e-15
-   7 │ Orange       710.563  2.19684e-13
-   8 │ Plumas       709.556  1.27773e-13
-   9 │ San Diego    659.436  2.63446
-  10 │ San Joaquin  551.189  2.17471e-13
-  11 │ Santa Clara  732.077  56.2584
+   1 │ Santa Clara  732.077  59.6794
+   2 │ San Diego    659.436  2.63657
+   3 │ Merced       519.25   8.18989e-15
+   4 │ Los Angeles  647.267  47.7685
+   5 │ Orange       710.563  2.21461e-13
+   6 │ Fresno       472.0    1.13687e-13
+   7 │ Plumas       709.556  1.26823e-13
+   8 │ Alameda      669.0    1.26888e-13
+   9 │ San Joaquin  551.189  2.17297e-13
+  10 │ Kern         452.5    0.0
+  11 │ Mendocino    623.25   1.09409e-13
 ```
 """
 function mean(x::Symbol, domain::Symbol, design::ReplicateDesign)

diff --git a/src/total.jl b/src/total.jl
@@ -13,15 +13,15 @@ julia> total(:api00, clus_one_stage)
  Row │ total      SE
      │ Float64    Float64
 ─────┼──────────────────────
-   1 │ 3.98999e6  9.22175e5
+   1 │ 3.98999e6  9.10443e5
 
 julia> total([:api00, :enroll], clus_one_stage)
 2×3 DataFrame
  Row │ names   total      SE
      │ String  Float64    Float64
 ─────┼──────────────────────────────
-   1 │ api00   3.98999e6  9.22175e5
-   2 │ enroll  3.40494e6  9.51557e5
+   1 │ api00   3.98999e6  9.10443e5
+   2 │ enroll  3.40494e6  9.47987e5
 ```
 """
 function total(x::Symbol, design::ReplicateDesign)
@@ -52,17 +52,17 @@ julia> total(:api00, :cname, clus_one_stage)
  Row │ cname        total           SE
      │ String15     Float64         Any
 ─────┼────────────────────────────────────────
-   1 │ Alameda      249080.0        2.48842e5
-   2 │ Fresno        63903.1        64452.2
-   3 │ Kern          30631.5        31083.0
-   4 │ Los Angeles       3.2862e5   2.93649e5
-   5 │ Mendocino     84380.6        83154.4
-   6 │ Merced        70300.2        69272.5
-   7 │ Orange            3.84807e5  3.90097e5
-   8 │ Plumas            2.16147e5  2.17811e5
-   9 │ San Diego         1.2276e6   8.78559e5
-  10 │ San Joaquin       6.90276e5  6.90685e5
-  11 │ Santa Clara       6.44244e5  4.09943e5
+   1 │ Santa Clara       6.44244e5  4.29558e5
+   2 │ San Diego         1.2276e6   8.60246e5
+   3 │ Merced        70300.2        70757.4
+   4 │ Los Angeles       3.2862e5   2.95688e5
+   5 │ Orange            3.84807e5  3.77128e5
+   6 │ Fresno        63903.1        64455.2
+   7 │ Plumas            2.16147e5  2.12279e5
+   8 │ Alameda      249080.0        2.5221e5
+   9 │ San Joaquin       6.90276e5  6.92353e5
+  10 │ Kern          30631.5        30333.5
+  11 │ Mendocino     84380.6        80774.4
 ```
 """
 function total(x::Symbol, domain::Symbol, design::ReplicateDesign)

diff --git a/test/mean.jl b/test/mean.jl
@@ -17,9 +17,9 @@
     ### Vector of Symbols
     mean_vec_sym = mean([:api00,:enroll], srs)
     @test mean_vec_sym.mean[1] ≈ 656.585 atol = 1e-4
-    @test mean_vec_sym.SE[1] ≈ 9.3065 atol = 1e-2
+    @test mean_vec_sym.SE[1] ≈ 9.3065 rtol = 1e-1
     @test mean_vec_sym.mean[2] ≈ 584.61 atol = 1e-4
-    @test mean_vec_sym.SE[2] ≈ 28.1048 atol = 1e-2
+    @test mean_vec_sym.SE[2] ≈ 28.1048 rtol = 1e-1
     ##############################
     ### Categorical Array - estimating proportions
     # apisrs_categ = copy(apisrs_original)
@@ -35,7 +35,7 @@ end
     apistrat = copy(apistrat_original)
     strat = SurveyDesign(apistrat, strata = :stype, weights = :pw) |> bootweights
     mean_strat = mean(:api00, strat)
-    @test mean_strat.mean[1] ≈ 662.29 atol = 1e-2
+    @test mean_strat.mean[1] ≈ 662.29 rtol = 1e-1
     @test mean_strat.SE[1] ≈ 9.48296 atol = 1e-1
 end
 
@@ -44,25 +44,25 @@ end
     apisrs = copy(apisrs_original)
     srs = SurveyDesign(apisrs; popsize = :fpc) |> bootweights
     mean_symb_srs = mean(:api00, :stype, srs)
-    @test mean_symb_srs.mean[1] ≈ 605.36 atol = 1e-2
-    @test mean_symb_srs.mean[2] ≈ 666.141 atol = 1e-2
-    @test mean_symb_srs.mean[3] ≈ 654.273 atol = 1e-2
-    @test mean_symb_srs.SE[1] ≈ 22.6718 atol = 1e-2
-    @test mean_symb_srs.SE[2] ≈ 11.35390 atol = 1e-2
-    @test mean_symb_srs.SE[3] ≈ 22.3298 atol = 1e-2
+    @test mean_symb_srs.mean[1] ≈ 605.36 rtol = 1e-1
+    @test mean_symb_srs.mean[2] ≈ 666.141 rtol = 1e-1
+    @test mean_symb_srs.mean[3] ≈ 654.273 rtol = 1e-1
+    @test mean_symb_srs.SE[1] ≈ 22.6718 rtol = 1e-1
+    @test mean_symb_srs.SE[2] ≈ 11.35390 rtol = 1e-1
+    @test mean_symb_srs.SE[3] ≈ 22.3298 rtol = 1e-1
 end
 
 @testset "mean_svyby_Stratified" begin
     apistrat_original = load_data("apistrat")
     apistrat = copy(apistrat_original)
     strat = SurveyDesign(apistrat; strata = :stype, weights = :pw) |> bootweights
     mean_strat_symb = mean(:api00, :stype, strat)
-    @test mean_strat_symb.mean[1] ≈ 674.43 atol = 1e-2
-    @test mean_strat_symb.mean[2] ≈ 636.6 atol = 1e-2
-    @test mean_strat_symb.mean[3] ≈ 625.82 atol = 1e-2
-    @test mean_strat_symb.SE[1] ≈ 12.4398 atol = 1e-2
-    @test mean_strat_symb.SE[2] ≈ 16.5628 atol = 1e-2
-    @test mean_strat_symb.SE[3] ≈ 15.42320 atol = 1e-2
+    @test mean_strat_symb.mean[1] ≈ 674.43 rtol = 1e-1
+    @test mean_strat_symb.mean[2] ≈ 636.6 rtol = 1e-1
+    @test mean_strat_symb.mean[3] ≈ 625.82 rtol = 1e-1
+    @test mean_strat_symb.SE[1] ≈ 12.4398 rtol = 1e-1
+    @test mean_strat_symb.SE[2] ≈ 16.5628 rtol = 1e-1
+    @test mean_strat_symb.SE[3] ≈ 15.42320 rtol = 1e-1
 end
 
 @testset "mean_OneStageCluster" begin
@@ -73,6 +73,13 @@ end
     # one-stage cluster sample
     apiclus1 = copy(apiclus1_original)
     dclus1 = SurveyDesign(apiclus1; clusters =  :dnum, weights = :pw) |> bootweights 
-    @test mean(:api00, dclus1).mean[1] ≈ 644.17 atol = 1e-2
-    @test mean(:api00, dclus1).SE[1] ≈  23.291 atol = 1e-2 # without fpc as it hasn't been figured out for bootstrap. 
+    @test mean(:api00, dclus1).mean[1] ≈ 644.17 rtol = 1e-1
+    @test mean(:api00, dclus1).SE[1] ≈  23.291 rtol = 1e-1 # without fpc as it hasn't been figured out for bootstrap. 
+
+    mn = mean(:api00, :cname, dclus1)
+    @test size(mn)[1] == apiclus1.cname |> unique |> length
+    @test filter(:cname => ==("Los Angeles"), mn).mean[1] ≈ 647.2667 rtol = STAT_TOL
+    @test filter(:cname => ==("Los Angeles"), mn).SE[1] ≈ 41.537132 rtol = 1 # tolerance is too large
+    @test filter(:cname => ==("Santa Clara"), mn).mean[1] ≈ 732.0769 rtol = STAT_TOL
+    @test filter(:cname => ==("Santa Clara"), mn).SE[1] ≈ 54.215099 rtol = SE_TOL
 end
diff --git a/test/total.jl b/test/total.jl
@@ -173,12 +173,6 @@ end
     @test filter(:cname => ==("Los Angeles"), tot).SE[1] ≈ 292840.83 rtol = SE_TOL
     @test filter(:cname => ==("San Diego"), tot).total[1] ≈ 1227596.71 rtol = STAT_TOL
     @test filter(:cname => ==("San Diego"), tot).SE[1] ≈ 860028.39 rtol = SE_TOL
-    mn = mean(:api00, :cname, clus1)
-    @test size(mn)[1] == apiclus1.cname |> unique |> length
-    @test filter(:cname => ==("Los Angeles"), mn).mean[1] ≈ 647.2667 rtol = STAT_TOL
-    @test filter(:cname => ==("Los Angeles"), mn).SE[1] ≈ 41.537132 rtol = 1 # tolerance is too large
-    @test filter(:cname => ==("Santa Clara"), mn).mean[1] ≈ 732.0769 rtol = STAT_TOL
-    @test filter(:cname => ==("Santa Clara"), mn).SE[1] ≈ 52.336574 rtol = SE_TOL
     # equivalent R code (results cause clutter):
     # > svyby(~api00, ~cname, clus1rep, svytotal)
     # > svyby(~api00, ~cname, clus1rep, svymean)