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Add parallel-subsampling changes to merge.
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| using DataFrames, CSV, Statistics, Plots | ||
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| metrics = CSV.read("$res_path/metrics.csv", DataFrame) | ||
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| methods = reverse(unique(metrics.method)) | ||
| batch_sizes = unique(metrics.batch_size) | ||
| batch_size_prop = unique(metrics.batch_size_prop) | ||
| xticks_label = ("$b\n$(p*100)%" for (b, p) in zip(batch_sizes, batch_size_prop)) | ||
| colors = palette(:tab10) | ||
| metrics_cols = [:e_train_mae, :f_train_mae, :e_test_mae, :f_test_mae, :time] | ||
| metric_labels = ["E MAE | eV/atom", | ||
| "F MAE | eV/Å", | ||
| "E MAE | eV/atom", | ||
| "F MAE | eV/Å", | ||
| "Time | s"] | ||
| for (i, metric) in enumerate(metrics_cols) | ||
| plot() | ||
| for (j, method) in enumerate(methods) | ||
| metric_means = []; metric_se = [] | ||
| for batch_size in batch_sizes | ||
| ms = metrics[ metrics.method .== method .&& | ||
| metrics.batch_size .== batch_size , metric] | ||
| m = mean(ms) | ||
| se = stdm(ms, m) / sqrt(length(ms)) # standard error | ||
| push!(metric_means, m) | ||
| push!(metric_se, se) | ||
| end | ||
| plot!(batch_sizes, | ||
| metric_means, | ||
| ribbon = metric_se, | ||
| color = colors[j], | ||
| fillalpha=.1, | ||
| label=method) | ||
| plot!(batch_sizes, | ||
| metric_means, | ||
| seriestype = :scatter, | ||
| thickness_scaling = 1.35, | ||
| markersize = 3, | ||
| markerstrokewidth = 0, | ||
| markerstrokecolor = :black, | ||
| markercolor = colors[j], | ||
| label="") | ||
| max = metric == :time ? 4000 : 1 | ||
| min = metric == :time ? -100 : minimum(metric_means) * 0.99 | ||
| plot!(dpi = 300, | ||
| label = "", | ||
| xscale=:log2, | ||
| xticks = (batch_sizes, xticks_label), | ||
| ylim=(min, max), | ||
| xlabel = "Training Dataset Sample Size", | ||
| ylabel = metric_labels[i]) | ||
| end | ||
| savefig("$res_path/$metric.png") | ||
| end | ||
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| # xformatter = :scientific, | ||
| # markershape = :circle, | ||
| # markercolor = :gray | ||
| # yerror=metric_std, | ||
| #ribbon=metric_std, | ||
| #yerror=metric_std, | ||
| # markerstrokewidth=0, markersize=5, | ||
| #yaxis=:log, | ||
| #xaxis=:log2, yaxis=:log, | ||
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| #for metric in [:e_train_mae, :f_train_mae, :e_test_mae, :f_test_mae, :time] | ||
| # scatter() | ||
| # for method in reverse(unique(metrics[:, :method])[1:end]) | ||
| # batch_size_vals = metrics[metrics.method .== method, :][:, :batch_size] | ||
| # metric_vals = metrics[metrics.method .== method, :][:, metric] | ||
| # scatter!(batch_size_vals, metric_vals, label = method, | ||
| # alpha = 0.5, dpi=300, markerstrokewidth=0, markersize=5, xaxis=:log2, yaxis=:log, | ||
| # xlabel = "Sample size", | ||
| # ylabel = "$metric") | ||
| # end | ||
| # savefig("$res_path/$metric-srs.png") | ||
| #end | ||
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| #scatter() | ||
| #for method in reverse(unique(metrics[:, :method])[2:end]) | ||
| # batch_size_vals = metrics[metrics.method .== method, :][:, :batch_size] | ||
| # speedup_vals = metrics[metrics.method .== "DPP", :][:, :time] ./ | ||
| # metrics[metrics.method .== method, :][:, :time] | ||
| # scatter!(batch_size_vals, speedup_vals, label = "DPP time / $method time", | ||
| # alpha = 0.5, dpi=300, markerstrokewidth=0, markersize=5, xaxis=:log2, | ||
| # xlabel = "Sample size", | ||
| # ylabel = "Speedup") | ||
| #end | ||
| #savefig("$res_path/speedup-srs.png") | ||
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| #using DataFrames, CSV, Plots | ||
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| #metrics = CSV.read("metrics.csv", DataFrame) | ||
| #res_path = "dyomet/" | ||
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| #for metric in [:e_train_mae, :f_train_mae, :e_test_mae, :f_test_mae, :time] | ||
| # scatter() | ||
| # for method in reverse(unique(metrics[:, :method])[1:end]) | ||
| # batch_size_vals = metrics[metrics.method .== method, :][:, :batch_size] | ||
| # metric_vals = metrics[metrics.method .== method, :][:, metric] | ||
| # scatter!(batch_size_vals, metric_vals, label = method, | ||
| # alpha = 0.5, dpi=300, markerstrokewidth=0, markersize=5, xaxis=:log2, yaxis=:log, | ||
| # xlabel = "Sample size", | ||
| # ylabel = "$metric") | ||
| # end | ||
| # savefig("$res_path/$metric-srs.png") | ||
| #end | ||
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| #scatter() | ||
| #for method in reverse(unique(metrics[:, :method])[2:end]) | ||
| # batch_size_vals = metrics[metrics.method .== method, :][:, :batch_size] | ||
| # speedup_vals = metrics[metrics.method .== "DPP", :][:, :time] ./ | ||
| # metrics[metrics.method .== method, :][:, :time] | ||
| # scatter!(batch_size_vals, speedup_vals, label = "DPP time / $method time", | ||
| # alpha = 0.5, dpi=300, markerstrokewidth=0, markersize=5, xaxis=:log2, | ||
| # xlabel = "Sample size", | ||
| # ylabel = "Speedup") | ||
| #end | ||
| #savefig("$res_path/speedup-srs.png") | ||
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