working on tables

replication/debiasing/debiasing.jl

@@ -99,7 +99,7 @@ end
 
 function run_all()
     lambda_methods = instances(LambdaMethod)
-    lambda_multipliers = [0.9, 1.0]
+    lambda_multipliers = [1.0]
     lambda_candidates = [4.0, 5.0]
     n_subsample = 10
     d = 1

replication/debiasing/tables.jl

@@ -0,0 +1,131 @@
+using CSV
+using DataFrames
+
+function lambda_method_order(l)
+    if l == "optimal"
+        return 1
+    elseif l == "polynomial"
+        return 2
+    elseif l == "gcv"
+        return 3
+    end
+end
+
+function lambda_method_format(l)
+    if l == "optimal"
+        return "OPT"
+    elseif l == "polynomial"
+        return "POLY"
+    elseif l == "gcv"
+        return "GCV"
+    end
+end
+
+data = CSV.read("./replication/debiasing/results.csv", DataFrame)
+#println(names(data))
+#println(length(names(data)))
+data = select!(data, sort(names(data)))
+data = select!(data, [:d, :n, :J_estimator, :lambda_target, :lambda_method, :J_lifetime,
+                      :B_estimator, :B_lifetime, :n_subsample,
+                      :lambda_multiplier, :lambda, :rmse, :bias, :sd, :bias_over_sd,
+                      :bias_theory, :sd_theory, :coverage, :average_width])
+data = sort!(data, [:J_estimator, :lambda_target, order(:lambda_method, by=lambda_method_order),
+                    :J_lifetime, :d, :n, :B_estimator, :B_lifetime, :n_subsample,
+                    :lambda_multiplier])
+
+#data[!, :J_lifetime] = string.(data[!, :J_lifetime])
+#println(names(data))
+#println(length(names(data)))
+#data = sort(data, [:J_estimator, :lambda_target])
+display(data)
+
+function make_table(df)
+    tex = "\\begin{tabular}{cccccccccccccc}\n"
+    tex *= "\\hline\n"
+    tex *= "\$J\$ & LS & \$B\$ & \$\\lambda\$ & RMSE & Bias & SD & Bias/SD & OBias & OSD & CR & CIW \\\\\n"
+
+    display(df)
+    for i in 1:nrow(df)
+        row = df[i, :]
+
+        if i > 1 && df[i, :J_estimator] == df[i-1, :J_estimator]
+            tex *= ""
+        else
+            tex *= "$(df[i, :J_estimator])"
+        end
+
+        if i > 1 && df[i, :J_lifetime] == df[i-1, :J_lifetime] &&
+                     df[i, :lambda_method] == df[i-1, :lambda_method]
+            tex *= "&"
+        else
+            # TODO format nicely
+            #tex *= "& $(df[i, :J_lifetime]) $(df[i, :lambda_method])"
+            Jl = df[i, :J_lifetime]
+            lm_fmt = lambda_method_format(df[i, :lambda_method])
+            if lm_fmt != "OPT"
+                hat = "\\hat"
+            else
+                hat = ""
+            end
+            tex *= "& \$$hat\\lambda_{$Jl}^{\\scriptsize{\\textrm{$lm_fmt}}}\$"
+        end
+
+        for cell in df[i, [:B_estimator, :lambda, :rmse, :bias, :sd,
+                        :bias_over_sd, :bias_theory, :sd_theory,
+                        :coverage, :average_width]]
+            if isa(cell, Float64)
+                cell = round(cell, digits=3)
+            end
+            tex *= "& $cell"
+        end
+
+        tex *= "\\\\\n"
+    end
+    tex *= "\\end{tabular}"
+
+    #for row in eachrow(df_Je_Jl_lm[!, [:J_estimator, :J_lifetime, :B_estimator, :lambda, :rmse, :bias, :sd,
+                                       #:bias_over_sd, :bias_theory, :sd_theory,
+                                       #:coverage, :average_width]])
+
+    #for J_estimator in unique(df[!, "J_estimator"])
+        #tex *= "$J_estimator "
+        #df_Je = filter(:J_estimator => ==(J_estimator), df)
+        #for J_lifetime in unique(df_Je[!, "J_lifetime"])
+            #df_Je_Jl = filter(:J_lifetime => ==(J_lifetime), df)
+            #for lambda_method in unique(df_Je_Jl[!, "lambda_method"])
+                #df_Je_Jl_lm = filter(:lambda_method => ==(lambda_method), df_Je_Jl)
+                ## TODO format this nicely
+                #tex *= "$J_lifetime $lambda_method "
+                #for row in eachrow(df_Je_Jl_lm[!, [:B_estimator, :lambda, :rmse, :bias, :sd,
+                                                   #:bias_over_sd, :bias_theory, :sd_theory,
+                                                   #:coverage, :average_width]])
+                    #for cell in row
+                        #println(cell)
+                        #cell = round(cell, digits=3)
+                        #tex *= "& $cell"
+                    #end
+                    #tex *= "\\\\\n & "
+                #end
+            #end
+        #end
+    #end
+
+    #tex = chop(tex, tail = 3)
+    #tex *= "\\hline\n"
+    #tex *= "\\end{tabular}"
+
+    n = df[1, "n"]
+    d = df[1, "d"]
+    write("./replication/debiasing/table_d$(d)_n$(n).tex", tex)
+
+end
+
+for d in unique(data[!, "d"])
+    data_d = filter(:d => ==(d), data)
+    for n in unique(data_d[!, "n"])
+        println(n)
+        data_d_n = filter(:n => ==(n), data_d)
+        display(data_d_n)
+        make_table(data_d_n)
+    end
+end