updated kable function to produce latex table

Author: pat-alt

dev/notebooks/mitigation_strategies.qmd

@@ -24,7 +24,7 @@ models = [
 ]
 generators = Dict(
     :Generic=>GenericGenerator(decision_threshold=0.5),
-    :REVISE=>REVISEGenerator(),
+    :Latent=>REVISEGenerator(),
     :Generic_conservative=>GenericGenerator(decision_threshold=0.9),
     :Gravitational=>GravitationalGenerator(),
     :ClapROAR=>ClapROARGenerator()
@@ -164,19 +164,170 @@ using Serialization
 results = Serialization.deserialize(joinpath(output_path,"results_synthetic.jls"))
 ```
 
-### Plots
+```{julia}
+using Images
+line_charts = Dict()
+errorbar_charts = Dict()
+for (data_name, res) in results
+    plt = plot(res)
+    Images.save(joinpath(www_path, "line_chart_$(data_name).png"), plt)
+    line_charts[data_name] = plt
+    plt = plot(res,maximum(res.output.n))
+    Images.save(joinpath(www_path, "errorbar_chart_$(data_name).png"), plt)
+    errorbar_charts[data_name] = plt
+end
+```
+
+### Chart in paper
+
+```{julia}
+using DataFrames, Statistics
+df = results[:overlapping].output
+df = df[df.n .== maximum(df.n),:]
+gdf = groupby(df, [:generator, :model, :n, :name, :scope])
+df_plot = combine(gdf, :value => (x -> [(mean(x),mean(x)+std(x),mean(x)-std(x))]) => [:mean, :ymax, :ymin])
+df_plot = df_plot[[name in [:mmd, :mmd_grid, :model_performance] for name in df_plot.name],:]
+df_plot = df_plot[.!(df_plot.name.==:mmd .&& df_plot.scope.==:model),:]
+df_plot = mapcols(x -> typeof(x) == Vector{Symbol} ? string.(x) : x, df_plot)
+transform!(df_plot, :name => (X -> [x=="mmd" ? "MMD (domain)" : x for x in X]) => :name)
+transform!(df_plot, :name => (X -> [x=="mmd_grid" ? "MMD (model)" : x for x in X]) => :name)
+transform!(df_plot, :name => (X -> [x=="model_performance" ? "Performance" : x for x in X]) => :name)
+transform!(df_plot, :generator => (X -> [x=="Generic" ? "Generic (γ=0.5)" : x for x in X]) => :generator)
+transform!(df_plot, :generator => (X -> [x=="Generic_conservative" ? "Generic (γ=0.9)" : x for x in X]) => :generator)
+
+ncol = length(unique(df_plot.model))
+nrow = length(unique(df_plot.name))
+
+using RCall
+scale_ = 2.2
+R"""
+library(ggplot2)
+plt <- ggplot($df_plot) +
+    geom_bar(aes(x=n, y=mean, fill=generator), stat="identity", alpha=0.5, position="dodge") +
+    geom_pointrange( aes(x=n, y=mean, ymin=ymin, ymax=ymax, colour=generator), alpha=0.9, position=position_dodge(width=0.9), size=0.5) +
+    facet_wrap(name ~ model, scale="free_y", ncol=$ncol) +
+    labs(y = "Value") + 
+    scale_fill_discrete(name="Generator:") +
+    scale_colour_discrete(name="Generator:") +
+    theme(
+        axis.title.x=element_blank(),
+        axis.text.x=element_blank(),
+        axis.ticks.x=element_blank()
+    )
+temp_path <- file.path(tempdir(), "plot.png")
+ggsave(temp_path,width=$ncol * $scale_,height=$nrow * $scale_ * 0.75) 
+"""
+
+img = Images.load(rcopy(R"temp_path"))
+Images.save(joinpath(www_path,"paper_synthetic_results.png"), img)
+```
+
+
+### Latent Space Search
 
 ```{julia}
+generators = Dict(
+    :Latent=>GenericGenerator(decision_threshold=0.5),
+    :Latent_conservative=>GenericGenerator(decision_threshold=0.9),
+    :Gravitational=>GravitationalGenerator(),
+    :ClapROAR=>ClapROARGenerator()
+)
+```
+
+```{julia}
+experiments = set_up_experiments(data_sets,models,generators)
+```
+
+```{julia}
+n_evals = 5
+n_rounds = 50
+evaluate_every = Int(round(n_rounds/n_evals))
+n_folds = 5
+n_bootstrap = 1
+T = 100
+using Serialization
+results = run_experiments(
+    experiments;
+    save_path=output_path,evaluate_every=evaluate_every,n_rounds=n_rounds, n_folds=n_folds, n_bootstrap=n_bootstrap, T=T
+)
+Serialization.serialize(joinpath(output_path,"results_synthetic_latent.jls"),results)
+```
+
+```{julia}
+using Serialization
+results = Serialization.deserialize(joinpath(output_path,"results_synthetic_latent.jls"))
+```
+
+```{julia}
+using Images
 line_charts = Dict()
 errorbar_charts = Dict()
 for (data_name, res) in results
-    line_charts[data_name] = plot(res)
-    errorbar_charts[data_name] = plot(res,50)
+    plt = plot(res)
+    Images.save(joinpath(www_path, "line_chart_latent_$(data_name).png"), plt)
+    line_charts[data_name] = plt
+    plt = plot(res,maximum(res.output.n))
+    Images.save(joinpath(www_path, "errorbar_chart_latent_$(data_name).png"), plt)
+    errorbar_charts[data_name] = plt
 end
 ```
 
+### Chart in paper
+
+```{julia}
+using DataFrames, Statistics
+df = results[:overlapping].output
+df = df[df.n .== maximum(df.n),:]
+gdf = groupby(df, [:generator, :model, :n, :name, :scope])
+df_plot = combine(gdf, :value => (x -> [(mean(x),mean(x)+std(x),mean(x)-std(x))]) => [:mean, :ymax, :ymin])
+df_plot = df_plot[[name in [:mmd, :mmd_grid, :model_performance] for name in df_plot.name],:]
+df_plot = df_plot[.!(df_plot.name.==:mmd .&& df_plot.scope.==:model),:]
+df_plot = mapcols(x -> typeof(x) == Vector{Symbol} ? string.(x) : x, df_plot)
+transform!(df_plot, :name => (X -> [x=="mmd" ? "MMD (domain)" : x for x in X]) => :name)
+transform!(df_plot, :name => (X -> [x=="mmd_grid" ? "MMD (model)" : x for x in X]) => :name)
+transform!(df_plot, :name => (X -> [x=="model_performance" ? "Performance" : x for x in X]) => :name)
+transform!(df_plot, :generator => (X -> [x=="Latent" ? "Latent (γ=0.5)" : x for x in X]) => :generator)
+transform!(df_plot, :generator => (X -> [x=="Latent_conservative" ? "Latent (γ=0.9)" : x for x in X]) => :generator)
+
+ncol = length(unique(df_plot.model))
+nrow = length(unique(df_plot.name))
+
+using RCall
+scale_ = 2.2
+R"""
+library(ggplot2)
+plt <- ggplot($df_plot) +
+    geom_bar(aes(x=n, y=mean, fill=generator), stat="identity", alpha=0.5, position="dodge") +
+    geom_pointrange( aes(x=n, y=mean, ymin=ymin, ymax=ymax, colour=generator), alpha=0.9, position=position_dodge(width=0.9), size=0.5) +
+    facet_wrap(name ~ model, scale="free_y", ncol=$ncol) +
+    labs(y = "Value") + 
+    scale_fill_discrete(name="Generator:") +
+    scale_colour_discrete(name="Generator:") +
+    theme(
+        axis.title.x=element_blank(),
+        axis.text.x=element_blank(),
+        axis.ticks.x=element_blank()
+    )
+temp_path <- file.path(tempdir(), "plot.png")
+ggsave(temp_path,width=$ncol * $scale_,height=$nrow * $scale_ * 0.75) 
+"""
+
+img = Images.load(rcopy(R"temp_path"))
+Images.save(joinpath(www_path,"paper_synthetic_latent_results.png"), img)
+```
+
 ## Real World
 
+```{julia}
+generators = Dict(
+    :Generic=>GenericGenerator(decision_threshold=0.5),
+    :Latent=>REVISEGenerator(),
+    :Generic_conservative=>GenericGenerator(decision_threshold=0.9),
+    :Gravitational=>GravitationalGenerator(),
+    :ClapROAR=>ClapROARGenerator()
+)
+```
+
 ```{julia}
 max_obs = 1000
 data_sets = AlgorithmicRecourseDynamics.Data.load_real_world(max_obs)
@@ -207,7 +358,7 @@ n_rounds = 50
 evaluate_every = Int(round(n_rounds/n_evals))
 n_folds = 5
 n_bootstrap = 1
-T = 100
+T = 250
 using Serialization
 results = run_experiments(
     experiments;
@@ -227,11 +378,16 @@ results = Serialization.deserialize(joinpath(output_path,"results_real_world.jls
 ```
 
 ```{julia}
+using Images
 line_charts = Dict()
 errorbar_charts = Dict()
 for (data_name, res) in results
-    line_charts[data_name] = plot(res)
-    errorbar_charts[data_name] = plot(res,50)
+    plt = plot(res)
+    Images.save(joinpath(www_path, "line_chart_$(data_name).png"), plt)
+    line_charts[data_name] = plt
+    plt = plot(res,maximum(res.output.n))
+    Images.save(joinpath(www_path, "errorbar_chart_$(data_name).png"), plt)
+    errorbar_charts[data_name] = plt
 end
 ```
 
@@ -262,7 +418,7 @@ n_rounds = 50
 evaluate_every = Int(round(n_rounds/n_evals))
 n_folds = 5
 n_bootstrap = 1
-T = 100
+T = 200
 using Serialization
 results = run_experiments(
     experiments;
@@ -282,11 +438,21 @@ Serialization.serialize(joinpath(output_path,"results_latent.jls"),results)
 ### Plots
 
 ```{julia}
+using Serialization
+results = Serialization.deserialize(joinpath(output_path,"results_real_world.jls"))
+```
+
+```{julia}
+using Images
 line_charts = Dict()
 errorbar_charts = Dict()
 for (data_name, res) in results
-    line_charts[data_name] = plot(res)
-    errorbar_charts[data_name] = plot(res,50)
+    plt = plot(res)
+    Images.save(joinpath(www_path, "line_chart_latent_$(data_name).png"), plt)
+    line_charts[data_name] = plt
+    plt = plot(res,maximum(res.output.n))
+    Images.save(joinpath(www_path, "errorbar_chart_latent_$(data_name).png"), plt)
+    errorbar_charts[data_name] = plt
 end
 ```
 

dev/notebooks/real_world.qmd

@@ -55,15 +55,15 @@ experiments = set_up_experiments(
 )
 ```
 
-## Strict convergence
+## Running Experiment
 
 ```{julia}
 n_evals = 5
 n_rounds = 50
 evaluate_every = Int(round(n_rounds/n_evals))
 n_folds = 5
 n_bootstrap = 1
-T = 100
+T = 250
 generative_model_params = (epochs=250, latent_dim=8)
 using Serialization
 results = run_experiments(
@@ -80,13 +80,26 @@ results = Serialization.deserialize(joinpath(output_path,"results.jls"))
 ```
 
 ```{julia}
+using Images
 line_charts = Dict()
 errorbar_charts = Dict()
 for (data_name, res) in results
-    line_charts[data_name] = plot(res)
-    errorbar_charts[data_name] = plot(res,50)
+    plt = plot(res)
+    Images.save(joinpath(www_path, "line_chart_$(data_name).png"), plt)
+    line_charts[data_name] = plt
+    plt = plot(res,maximum(res.output.n))
+    Images.save(joinpath(www_path, "errorbar_chart_$(data_name).png"), plt)
+    errorbar_charts[data_name] = plt
 end
 ```
 
+### Table in paper
+
+```{julia}
+using AlgorithmicRecourseDynamics: kable
+kable(results, [50])
+```
+
+
 
 

dev/notebooks/results.qmd

@@ -94,10 +94,9 @@ T = 100
 using Serialization
 results = run_experiments(
     experiments;
-    save_path=output_path,evaluate_every=evaluate_every,n_rounds=n_rounds, n_folds=n_folds, n_bootstrap=n_bootstrap, T=T,
-    convergence=:strict, save_name_suffix="strict"
+    save_path=output_path,evaluate_every=evaluate_every,n_rounds=n_rounds, n_folds=n_folds, n_bootstrap=n_bootstrap, T=T
 )
-Serialization.serialize(joinpath(output_path,"results_strict.jls"),results)
+Serialization.serialize(joinpath(output_path,"results.jls"),results)
 ```
 
 ```{julia}
@@ -155,26 +154,71 @@ end
 plot_dict = Dict(key => reduce(vcat, [plots[key] for plots in values(plot_dict)]) for (key, value) in generators)
 for (name, plts) in plot_dict
     plt = plot(plts..., layout=(length(choices),length(models)),size=(length(choices)*300,length(models)*300))
-    savefig(plt, joinpath(www_path,"models_train_after_$(name)_strict.png"))
+    savefig(plt, joinpath(www_path,"models_train_after_$(name).png"))
 end
 ```
 
 ```{julia}
 using Serialization
-results = Serialization.deserialize(joinpath(output_path,"results_strict.jls"))
+results = Serialization.deserialize(joinpath(output_path,"results.jls"))
 ```
 
 ### Plots
 
 ```{julia}
+using Images
 line_charts = Dict()
 errorbar_charts = Dict()
 for (data_name, res) in results
     plt = plot(res)
-    savefig(plt, joinpath(www_path, "line_chart_$(data_name).png"))
+    Images.save(joinpath(www_path, "line_chart_$(data_name).png"), plt)
     line_charts[data_name] = plt
     plt = plot(res,maximum(res.output.n))
-    savefig(plt, joinpath(www_path, "errorbar_chart_$(data_name).png"))
+    Images.save(joinpath(www_path, "errorbar_chart_$(data_name).png"), plt)
     errorbar_charts[data_name] = plt
 end
 ```
+
+### Chart in paper
+
+```{julia}
+using DataFrames, Statistics
+df = results[:overlapping].output
+df = df[df.n .== maximum(df.n),:]
+gdf = groupby(df, [:generator, :model, :n, :name, :scope])
+df_plot = combine(gdf, :value => (x -> [(mean(x),mean(x)+std(x),mean(x)-std(x))]) => [:mean, :ymax, :ymin])
+df_plot = df_plot[[name in [:mmd, :mmd_grid, :model_performance] for name in df_plot.name],:]
+df_plot = df_plot[.!(df_plot.name.==:mmd .&& df_plot.scope.==:model),:]
+df_plot = mapcols(x -> typeof(x) == Vector{Symbol} ? string.(x) : x, df_plot)
+transform!(df_plot, :name => (X -> [x=="mmd" ? "MMD (domain)" : x for x in X]) => :name)
+transform!(df_plot, :name => (X -> [x=="mmd_grid" ? "MMD (model)" : x for x in X]) => :name)
+transform!(df_plot, :name => (X -> [x=="model_performance" ? "Performance" : x for x in X]) => :name)
+
+ncol = length(unique(df_plot.model))
+nrow = length(unique(df_plot.name))
+
+using RCall
+scale_ = 2
+R"""
+library(ggplot2)
+plt <- ggplot($df_plot) +
+    geom_bar(aes(x=n, y=mean, fill=generator), stat="identity", alpha=0.5, position="dodge") +
+    geom_pointrange( aes(x=n, y=mean, ymin=ymin, ymax=ymax, colour=generator), alpha=0.9, position=position_dodge(width=0.9), size=0.5) +
+    facet_wrap(name ~ model, scale="free_y", ncol=$ncol) +
+    labs(y = "Value") + 
+    scale_fill_discrete(name="Generator:") +
+    scale_colour_discrete(name="Generator:") +
+    theme(
+        axis.title.x=element_blank(),
+        axis.text.x=element_blank(),
+        axis.ticks.x=element_blank()
+    )
+temp_path <- file.path(tempdir(), "plot.png")
+ggsave(temp_path,width=$ncol * $scale_,height=$nrow * $scale_ * 0.8) 
+"""
+
+img = Images.load(rcopy(R"temp_path"))
+Images.save(joinpath(www_path,"paper_synthetic_results.png"), img)
+```
+
+