added new empty docs pages for emulator examples\

docs/make.jl

@@ -6,7 +6,15 @@ using Documenter
 
 #----------
 
-examples = ["Lorenz example" => "examples/lorenz_example.md", "Turbulence example" => "examples/edmf_example.md"]
+examples = [
+    "Emulator testing" => [
+        "Regression $\mathbb{R}^2 -> \mathbb{R}^2$" => "examples/emulators/regression_2d_2d.md",
+        "Lorenz Integrator $\mathbb{R}^3 -> \mathbb{R}^3$" => "examples/emulators/lorenz_integrator_3d_3d.md",
+        "Global Sensitivity Analysis $\mathbb{R}^3 -> \mathbb{R}$" => "examples/emulators/ishigami_3d_1d.md",
+    ]
+    "Lorenz example" => "examples/lorenz_example.md",
+    "Turbulence example" => "examples/edmf_example.md"
+]
 
 design = ["AbstractMCMC sampling API" => "API/AbstractMCMC.md"]
 

docs/src/examples/emulators/ishigami_3d_1d.md

@@ -0,0 +1 @@
+# Global Sensitiviy Analysis for an emulated Ishigami function 

docs/src/examples/emulators/lorenz_integrator_3d_3d.md

@@ -0,0 +1 @@
+# Integrating Lorenz 63 with an Emulated integrator

docs/src/examples/emulators/regression_2d_2d.md

@@ -0,0 +1 @@
+# Regression of $\mathbb{R}^2 \to \mathbb{R}^2$ smooth function

examples/Emulator/Regression_2d_2d/vector_optimize_and_plot_RF.jl~

@@ -0,0 +1,257 @@
+# Reference the in-tree version of CalibrateEmulateSample on Julias load path
+include(joinpath(@__DIR__, "..", "..", "ci", "linkfig.jl"))
+
+# Import modules
+using Random
+using StableRNGs
+using Distributions
+using Statistics
+using LinearAlgebra
+using CalibrateEmulateSample.Emulators
+using CalibrateEmulateSample.DataContainers
+using CalibrateEmulateSample.ParameterDistributions
+
+
+plot_flag = true
+if plot_flag
+    using Plots
+    gr(size = (1500, 700))
+    Plots.scalefontsizes(1.3)
+    font = Plots.font("Helvetica", 18)
+    fontdict = Dict(:guidefont => font, :xtickfont => font, :ytickfont => font, :legendfont => font)
+
+end
+
+function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T}) where {T}
+    m, n = length(vy), length(vx)
+    gx = reshape(repeat(vx, inner = m, outer = 1), m, n)
+    gy = reshape(repeat(vy, inner = 1, outer = n), m, n)
+
+    return gx, gy
+end
+rng_seed = 41
+Random.seed!(rng_seed)
+output_directory = joinpath(@__DIR__, "output")
+if !isdir(output_directory)
+    mkdir(output_directory)
+end
+
+#problem
+n = 100  # number of training points
+p = 2   # input dim 
+d = 2   # output dim
+X = 2.0 * π * rand(p, n)
+# G(x1, x2)
+g1x = sin.(X[1, :]) .+ cos.(X[2, :])
+g2x = sin.(X[1, :]) .- cos.(X[2, :])
+gx = zeros(2, n)
+gx[1, :] = g1x
+gx[2, :] = g2x
+
+# Add noise η
+μ = zeros(d)
+Σ = 0.1 * [[0.8, 0.1] [0.1, 0.5]] # d x d
+noise_samples = rand(MvNormal(μ, Σ), n)
+# y = G(x) + η
+Y = gx .+ noise_samples
+
+iopairs = PairedDataContainer(X, Y, data_are_columns = true)
+@assert get_inputs(iopairs) == X
+@assert get_outputs(iopairs) == Y
+
+#plot training data with and without noise
+if plot_flag
+    p1 = plot(
+        X[1, :],
+        X[2, :],
+        g1x,
+        st = :surface,
+        camera = (30, 60),
+        c = :cividis,
+        xlabel = "x1",
+        ylabel = "x2",
+        zguidefontrotation = 90,
+    )
+
+    figpath = joinpath(output_directory, "RF_test_observed_y1nonoise.png")
+    savefig(figpath)
+
+    p2 = plot(
+        X[1, :],
+        X[2, :],
+        g2x,
+        st = :surface,
+        camera = (30, 60),
+        c = :cividis,
+        xlabel = "x1",
+        ylabel = "x2",
+        zguidefontrotation = 90,
+    )
+    figpath = joinpath(output_directory, "RF_test_observed_y2nonoise.png")
+    savefig(figpath)
+
+    p3 = plot(
+        X[1, :],
+        X[2, :],
+        Y[1, :],
+        st = :surface,
+        camera = (30, 60),
+        c = :cividis,
+        xlabel = "x1",
+        ylabel = "x2",
+        zguidefontrotation = 90,
+    )
+    figpath = joinpath(output_directory, "RF_test_observed_y1.png")
+    savefig(figpath)
+
+    p4 = plot(
+        X[1, :],
+        X[2, :],
+        Y[2, :],
+        st = :surface,
+        camera = (30, 60),
+        c = :cividis,
+        xlabel = "x1",
+        ylabel = "x2",
+        zguidefontrotation = 90,
+    )
+    figpath = joinpath(output_directory, "RF_test_observed_y2.png")
+    savefig(figpath)
+
+end
+
+# setup random features
+n_features = 200
+
+
+vrfi = VectorRandomFeatureInterface(n_features, p, d)
+
+emulator = Emulator(vrfi, iopairs, obs_noise_cov = Σ, normalize_inputs = true)
+println("build RF with $n training points and $(n_features) random features.")
+
+optimize_hyperparameters!(emulator) # although RF already optimized
+
+# Plot mean and variance of the predicted observables y1 and y2
+# For this, we generate test points on a x1-x2 grid.
+n_pts = 200
+x1 = range(0.0, stop = 4.0 / 5.0 * 2 * π, length = n_pts)
+x2 = range(0.0, stop = 4.0 / 5.0 * 2 * π, length = n_pts)
+X1, X2 = meshgrid(x1, x2)
+# Input for predict has to be of size N_samples x input_dim
+inputs = permutedims(hcat(X1[:], X2[:]), (2, 1))
+
+rf_mean, rf_cov = predict(emulator, inputs, transform_to_real = true)
+println("end predictions at ", n_pts * n_pts, " points")
+
+
+#plot predictions
+for y_i in 1:d
+    rf_var_temp = [diag(rf_cov[j]) for j in 1:length(rf_cov)] # (40000,)
+    rf_var = permutedims(vcat([x' for x in rf_var_temp]...), (2, 1)) # 2 x 40000
+
+    mean_grid = reshape(rf_mean[y_i, :], n_pts, n_pts) # 2 x 40000
+    if plot_flag
+        p5 = plot(
+            x1,
+            x2,
+            mean_grid,
+            st = :surface,
+            camera = (30, 60),
+            c = :cividis,
+            xlabel = "x1",
+            ylabel = "x2",
+            zlabel = "mean of y" * string(y_i),
+            zguidefontrotation = 90,
+        )
+    end
+    var_grid = reshape(rf_var[y_i, :], n_pts, n_pts)
+    if plot_flag
+        p6 = plot(
+            x1,
+            x2,
+            var_grid,
+            st = :surface,
+            camera = (30, 60),
+            c = :cividis,
+            xlabel = "x1",
+            ylabel = "x2",
+            zlabel = "var of y" * string(y_i),
+            zguidefontrotation = 90,
+        )
+
+        plot(p5, p6, layout = (1, 2), legend = false)
+
+        savefig(joinpath(output_directory, "RF_test_y" * string(y_i) * "_predictions.png"))
+    end
+end
+
+# Plot the true components of G(x1, x2)
+g1_true = sin.(inputs[1, :]) .+ cos.(inputs[2, :])
+g1_true_grid = reshape(g1_true, n_pts, n_pts)
+if plot_flag
+    p7 = plot(
+        x1,
+        x2,
+        g1_true_grid,
+        st = :surface,
+        camera = (30, 60),
+        c = :cividis,
+        xlabel = "x1",
+        ylabel = "x2",
+        zlabel = "sin(x1) + cos(x2)",
+        zguidefontrotation = 90,
+    )
+    savefig(joinpath(output_directory, "RF_test_true_g1.png"))
+end
+
+g2_true = sin.(inputs[1, :]) .- cos.(inputs[2, :])
+g2_true_grid = reshape(g2_true, n_pts, n_pts)
+if plot_flag
+    p8 = plot(
+        x1,
+        x2,
+        g2_true_grid,
+        st = :surface,
+        camera = (30, 60),
+        c = :cividis,
+        xlabel = "x1",
+        ylabel = "x2",
+        zlabel = "sin(x1) - cos(x2)",
+        zguidefontrotation = 90,
+    )
+    g_true_grids = [g1_true_grid, g2_true_grid]
+
+    savefig(joinpath(output_directory, "RF_test_true_g2.png"))
+
+end
+
+# Plot the difference between the truth and the mean of the predictions
+for y_i in 1:d
+
+    # Reshape rf_cov to size N_samples x output_dim
+    rf_var_temp = [diag(rf_cov[j]) for j in 1:length(rf_cov)] # (40000,)
+    rf_var = permutedims(vcat([x' for x in rf_var_temp]...), (2, 1)) # 40000 x 2
+
+    mean_grid = reshape(rf_mean[y_i, :], n_pts, n_pts)
+    var_grid = reshape(rf_var[y_i, :], n_pts, n_pts)
+    # Compute and plot 1/variance * (truth - prediction)^2
+
+    if plot_flag
+        zlabel = "1/var * (true_y" * string(y_i) * " - predicted_y" * string(y_i) * ")^2"
+
+        p9 = plot(
+            x1,
+            x2,
+            sqrt.(1.0 ./ var_grid .* (g_true_grids[y_i] .- mean_grid) .^ 2),
+            st = :surface,
+            camera = (30, 60),
+            c = :magma,
+            zlabel = zlabel,
+            xlabel = "x1",
+            ylabel = "x2",
+            zguidefontrotation = 90,
+        )
+
+        savefig(joinpath(output_directory, "RF_test_y" * string(y_i) * "_difference_truth_prediction.png"))
+    end
+end