{ "cells": [ { "cell_type": "markdown", "source": [ "# Mauna Loa time series example\n", "\n", "*You are seeing the\n", "notebook output generated by\n", "[Literate.jl](https://github.com/fredrikekre/Literate.jl) from the\n", "[Julia source file](https://github.com/JuliaGaussianProcesses/EasyGPs.jl/blob/master/examples/0-mauna-loa/script.jl).\n", "The rendered HTML can be viewed [in the docs](https://juliagaussianprocesses.github.io/EasyGPs.jl/dev/examples/0-mauna-loa/).*" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "In this notebook, we apply Gaussian process regression to the Mauna Loa CO₂\n", "dataset. It is adapted from [the corresponding AbstractGPs.jl tutorial](\n", "https://juliagaussianprocesses.github.io/AbstractGPs.jl/stable/examples/1-mauna-loa/).\n", "It is therefore instructive to read that first and then see how EasyGPs.jl\n", "simplifies the steps." ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Setup\n", "\n", "We make use of the following packages:" ], "metadata": {} }, { "outputs": [], "cell_type": "code", "source": [ "using CSV, DataFrames # data loading\n", "using EasyGPs # handles all things related to GPs\n", "using Plots # visualisation" ], "metadata": {}, "execution_count": 1 }, { "cell_type": "markdown", "source": [ "Let's load and visualize the dataset." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=2}", "image/png": 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"\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "execution_count": 2 } ], "cell_type": "code", "source": [ "(xtrain, ytrain), (xtest, ytest) = let\n", " data = CSV.read(joinpath(\"/home/runner/work/EasyGPs.jl/EasyGPs.jl/examples/0-mauna-loa\", \"CO2_data.csv\"), Tables.matrix; header=0)\n", " year = data[:, 1]\n", " co2 = data[:, 2]\n", "\n", " # We split the data into training and testing set:\n", " idx_train = year .< 2004\n", " idx_test = .!idx_train\n", "\n", " (year[idx_train], co2[idx_train]), (year[idx_test], co2[idx_test]) # block's return value\n", "end\n", "\n", "function plotdata()\n", " plot(; xlabel=\"year\", ylabel=\"CO₂ [ppm]\", legend=:bottomright)\n", " scatter!(xtrain, ytrain; label=\"training data\", ms=2, markerstrokewidth=0)\n", " return scatter!(xtest, ytest; label=\"test data\", ms=2, markerstrokewidth=0)\n", "end\n", "\n", "plotdata()" ], "metadata": {}, "execution_count": 2 }, { "cell_type": "markdown", "source": [ "## Prior\n", "\n", "We construct the GP prior using the same kernels and initial parameters as in the\n", "original tutorial." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t- σ² = 2980.9579870417283\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- σ² = 7.38905609893065\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.018315638888734182)\n\tRational Quadratic Kernel (α = 0.36787944117144233, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 1.0)\n\t\t\t- σ² = 1.0\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 7.3890560989306495)\n\t\t\t- σ² = 0.01831563888873418\n\tWhite Kernel\n\t\t\t- σ² = 0.01831563888873418" }, "metadata": {}, "execution_count": 3 } ], "cell_type": "code", "source": [ "k_smooth_trend = exp(8.0) * with_lengthscale(SEKernel(), exp(4.0))#with_lengthscale(SEKernel(), exp(4.0))\n", "k_seasonality =\n", " exp(2.0) * PeriodicKernel(; r=[0.5]) * with_lengthscale(SEKernel(), exp(4.0))\n", "k_medium_term_irregularities =\n", " 1.0 * with_lengthscale(RationalQuadraticKernel(; α=exp(-1.0)), 1.0)\n", "k_noise_terms =\n", " exp(-4.0) * with_lengthscale(SEKernel(), exp(-2.0)) + exp(-4.0) * WhiteKernel()\n", "kernel = k_smooth_trend + k_seasonality + k_medium_term_irregularities + k_noise_terms" ], "metadata": {}, "execution_count": 3 }, { "cell_type": "markdown", "source": [ "We construct the `GP` object with the kernel above:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.PeriodicKernel{Float64}, Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t- σ² = 2980.9579870417283\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- σ² = 7.38905609893065\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.018315638888734182)\n\tRational Quadratic Kernel (α = 0.36787944117144233, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 1.0)\n\t\t\t- σ² = 1.0\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 7.3890560989306495)\n\t\t\t- σ² = 0.01831563888873418\n\tWhite Kernel\n\t\t\t- σ² = 0.01831563888873418)" }, "metadata": {}, "execution_count": 4 } ], "cell_type": "code", "source": [ "gp = GP(kernel)" ], "metadata": {}, "execution_count": 4 }, { "cell_type": "markdown", "source": [ "## Posterior\n", "\n", "To construct a posterior, we can call the GP object with the usual AbstractGPs.jl API:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.PeriodicKernel{Float64}, Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{Float64}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.PeriodicKernel{Float64}, Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.018315638888734182)\n\t\t\t- σ² = 2980.9579870417283\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- σ² = 7.38905609893065\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.018315638888734182)\n\tRational Quadratic Kernel (α = 0.36787944117144233, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 1.0)\n\t\t\t- σ² = 1.0\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 7.3890560989306495)\n\t\t\t- σ² = 0.01831563888873418\n\tWhite Kernel\n\t\t\t- σ² = 0.01831563888873418), (α = [-9.508930876112435, 19.389883604424003, -7.197900320791052, -4.730615304800623, -15.234713648253596, 18.626206282434165, 7.52613866753285, -0.03155846892785483, -13.78865473660018, 1.6524756571158592 … -4.046597224913827, -2.933445740419233, 2.0990116246075754, 4.200011756113528, 5.696790413752951, -11.011994642514985, 9.404617185258822, -5.4955611580625705, 4.121759655599095, -1.660088986434353], C = LinearAlgebra.Cholesky{Float64, Matrix{Float64}}([54.675256509854954 54.657791476465945 … 38.45054512284434 38.41521522921302; 2988.4287692379394 1.3818485204982465 … 2.169551656459134 1.8834148423467896; … ; 2102.2934175352666 2104.619869227493 … 0.23177071515726433 0.19908837092736484; 2100.361746538508 2102.2934175352652 … 2988.428769237257 0.2317686016889045], 'U', 0), x = [1958.2083333333333, 1958.2916666666667, 1958.375, 1958.4583333333333, 1958.5416666666667, 1958.625, 1958.7083333333333, 1958.7916666666667, 1958.875, 1958.9583333333333 … 2003.2083333333333, 2003.2916666666667, 2003.375, 2003.4583333333333, 2003.5416666666667, 2003.625, 2003.7083333333333, 2003.7916666666667, 2003.875, 2003.9583333333333], δ = [315.71, 317.45, 317.5, 317.1, 315.86, 314.93, 313.2, 312.66, 313.33, 314.67 … 376.48, 377.74, 378.5, 378.18, 376.72, 374.31, 373.2, 373.1, 374.64, 375.93]))" }, "metadata": {}, "execution_count": 5 } ], "cell_type": "code", "source": [ "fpost_init = posterior(gp(xtrain), ytrain)" ], "metadata": {}, "execution_count": 5 }, { "cell_type": "markdown", "source": [ "Let's visualize what the GP fitted to the data looks like, for the initial choice of kernel hyperparameters.\n", "\n", "We use the following function to plot a GP `f` on a specific range, using the\n", "AbstractGPs [plotting\n", "recipes](https://juliagaussianprocesses.github.io/AbstractGPs.jl/dev/concrete_features/#Plotting).\n", "By setting `ribbon_scale=2` we visualize the uncertainty band with $\\pm 2$\n", "(instead of the default $\\pm 1$) standard deviations." ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=3}", "image/png": 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"\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n" ] }, "metadata": {}, "execution_count": 6 } ], "cell_type": "code", "source": [ "plot_gp!(f; label) = plot!(f(1920:0.2:2030); ribbon_scale=2, linewidth=1, label)\n", "\n", "plotdata()\n", "plot_gp!(fpost_init; label=\"posterior f(⋅)\")" ], "metadata": {}, "execution_count": 6 }, { "cell_type": "markdown", "source": [ "A reasonable fit to the data, but poor extrapolation away from the observations!" ], "metadata": {} }, { "cell_type": "markdown", "source": [ "## Hyperparameter Optimization\n", "\n", "We can now call `EasyGPs.fit` in order to optimize the hyperparameters. This takes care\n", "of all the parameterizations, automatic differentiation, and runs the optimizer for us.\n", "We pass an option to choose the exact same optimizer as in the original tutorial." ], "metadata": {} }, { "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " 31.334930 seconds (164.76 M allocations: 28.161 GiB, 55.54% gc time, 0.00% compilation time)\n" ] }, { "output_type": "execute_result", "data": { "text/plain": "AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.PeriodicKernel{Float64}, Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.006409359405350664)\n\t\t\t- σ² = 196189.1377809622\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- σ² = 6.031052736397145\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.01211762616986784)\n\tRational Quadratic Kernel (α = 0.0008271632559866195, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.04882049165706758)\n\t\t\t- σ² = 160.20288567771527\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 8.753051584372757)\n\t\t\t- σ² = 0.028319259298516024\n\tWhite Kernel\n\t\t\t- σ² = 0.035763467727454694)" }, "metadata": {}, "execution_count": 7 } ], "cell_type": "code", "source": [ "@time fitted_gp = EasyGPs.fit(\n", " gp,\n", " xtrain,\n", " ytrain;\n", " optimizer=Optim.LBFGS(;\n", " alphaguess=Optim.LineSearches.InitialStatic(; scaled=true),\n", " linesearch=Optim.LineSearches.BackTracking(),\n", " ),\n", ")" ], "metadata": {}, "execution_count": 7 }, { "cell_type": "markdown", "source": [ "Let's now construct the posterior GP with the optimized hyperparameters:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "AbstractGPs.PosteriorGP{AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.PeriodicKernel{Float64}, Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}, @NamedTuple{α::Vector{Float64}, C::LinearAlgebra.Cholesky{Float64, Matrix{Float64}}, x::Vector{Float64}, δ::Vector{Float64}}}(AbstractGPs.GP{AbstractGPs.ZeroMean{Float64}, KernelFunctions.KernelSum{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.KernelProduct{Tuple{KernelFunctions.ScaledKernel{KernelFunctions.PeriodicKernel{Float64}, Float64}, KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}}}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.RationalQuadraticKernel{Float64, Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.TransformedKernel{KernelFunctions.SqExponentialKernel{Distances.Euclidean}, KernelFunctions.ScaleTransform{Float64}}, Float64}, KernelFunctions.ScaledKernel{KernelFunctions.WhiteKernel, Float64}}}}(AbstractGPs.ZeroMean{Float64}(), Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.006409359405350664)\n\t\t\t- σ² = 196189.1377809622\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- σ² = 6.031052736397145\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.01211762616986784)\n\tRational Quadratic Kernel (α = 0.0008271632559866195, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.04882049165706758)\n\t\t\t- σ² = 160.20288567771527\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 8.753051584372757)\n\t\t\t- σ² = 0.028319259298516024\n\tWhite Kernel\n\t\t\t- σ² = 0.035763467727454694), (α = [-6.406182340634154, 9.479125493791651, -3.9490143583488737, -2.294487419227209, -7.264350222649295, 10.570794389574559, 4.76500548919227, -0.4504587431002098, -5.300917445548071, 0.6851966513533065 … -2.409231626659166, -1.8522007489649162, -0.09151094650266152, 2.9183720747914403, 3.859886698441741, -4.851671638375728, 5.789111919267359, -2.5957211978777988, 1.630186241122395, -1.560011051494671], C = LinearAlgebra.Cholesky{Float64, Matrix{Float64}}([443.1201144183181 443.11912202078764 … 424.54524962114675 424.4804049003455; 196354.9960507961 0.9378174820560204 … 31.32691542330711 31.08840895407605; … ; 188124.53958787597 188153.49719916153 … 0.28386305777042586 0.18248677810649572; 188095.8055877751 188124.53958787597 … 196354.9960507961 0.2838487116274963], 'U', 0), x = [1958.2083333333333, 1958.2916666666667, 1958.375, 1958.4583333333333, 1958.5416666666667, 1958.625, 1958.7083333333333, 1958.7916666666667, 1958.875, 1958.9583333333333 … 2003.2083333333333, 2003.2916666666667, 2003.375, 2003.4583333333333, 2003.5416666666667, 2003.625, 2003.7083333333333, 2003.7916666666667, 2003.875, 2003.9583333333333], δ = [315.71, 317.45, 317.5, 317.1, 315.86, 314.93, 313.2, 312.66, 313.33, 314.67 … 376.48, 377.74, 378.5, 378.18, 376.72, 374.31, 373.2, 373.1, 374.64, 375.93]))" }, "metadata": {}, "execution_count": 8 } ], "cell_type": "code", "source": [ "fpost_opt = posterior(fitted_gp(xtrain), ytrain)" ], "metadata": {}, "execution_count": 8 }, { "cell_type": "markdown", "source": [ "This is the kernel with the point-estimated hyperparameters:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Sum of 5 kernels:\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.006409359405350664)\n\t\t\t- σ² = 196189.1377809622\n\tProduct of 2 kernels:\n\t\t\tPeriodic Kernel, length(r) = 1\n\t\t\t\t\t- σ² = 6.031052736397145\n\t\t\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t\t\t- Scale Transform (s = 0.01211762616986784)\n\tRational Quadratic Kernel (α = 0.0008271632559866195, metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 0.04882049165706758)\n\t\t\t- σ² = 160.20288567771527\n\tSquared Exponential Kernel (metric = Distances.Euclidean(0.0))\n\t\t\t- Scale Transform (s = 8.753051584372757)\n\t\t\t- σ² = 0.028319259298516024\n\tWhite Kernel\n\t\t\t- σ² = 0.035763467727454694" }, "metadata": {}, "execution_count": 9 } ], "cell_type": "code", "source": [ "fpost_opt.prior.kernel" ], "metadata": {}, "execution_count": 9 }, { "cell_type": "markdown", "source": [ "And, finally, we can visualize our optimized posterior GP:" ], "metadata": {} }, { "outputs": [ { "output_type": "execute_result", "data": { "text/plain": "Plot{Plots.GRBackend() n=3}", "image/png": 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Package and system information
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\n", "Package information (click to expand)\n", "
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    "Status `~/work/EasyGPs.jl/EasyGPs.jl/examples/0-mauna-loa/Project.toml`\n",
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\n", "System information (click to expand)\n", "
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    "Julia Version 1.10.2\n",
    "Commit bd47eca2c8a (2024-03-01 10:14 UTC)\n",
    "Build Info:\n",
    "  Official https://julialang.org/ release\n",
    "Platform Info:\n",
    "  OS: Linux (x86_64-linux-gnu)\n",
    "  CPU: 4 × AMD EPYC 7763 64-Core Processor\n",
    "  WORD_SIZE: 64\n",
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    "Threads: 1 default, 0 interactive, 1 GC (on 4 virtual cores)\n",
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    "  JULIA_DEBUG = Documenter\n",
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