added RF emulator

update project toml examples for RF shuffle data update example example to produce comparable figs updates for compatability with CES 0.2.0 and RF 0.1.0 vector random feature support added fixes to ensure CES pipeline runs regularization and lorenz example format allows training with fewer features than data VRFI with SVD, and cholesky options feature num dep on n GCM example replace data multithreading and rng add ProgressBars remove high-level threading for now (takes place within LinAlg solvers) sbatch script truth at some points increased number optimization features default bugfix reg matrix argument initial tik-reg for EKI working TEKI 0 default eki, small tweaks add logdet complexity more consistent adding of definiteness chol/svd add logdet to scalar learning shape bug logdetI unite common functions in Random Feature, expand Scalar feature learning extend reverse svd for covs add diag terms to MatrixNormal description, default to diagonal regularizations rather than pos-def add diagonal option trimmed, and added const hp for diag cov compat with svd truncation, and more standard posdef corrections added scaling to complexity data change scalar interface lorenz 2d statsplot combine all MLT examples into this improved interfacing, unification and initial unit testing condensed into emulate_sample simplify scalar interface bug improved vector interface reg should be multiplicative! fixed small edits update ess.jl MSE on next ensemble, add input-diag case inflation optimizer defaults and cov representation inflation vec inflation utility for ensembles test pass with new defaults and cov structure format format add RF tests GP test fails resolved scalar_optimize_and_plot_RF.jl with new RF accel removed some abstract types, compatible with RandomFeatures 0.2.5 format typo another typo compatible with v0.3 RandomFeatures dispatching over RandomFeatures v0.3.1 multthread options typo more flexible priors compataility for SRF and RF v0.3.1 updates to GCM example scripts docstrings docstring API and format rm duplicate API docs format API docs work locally rename test pass format better messages, bugfix priors for diagonalized options emulation test scenarios for scalar and vector RF
CliMA · May 3, 2023 · e35cdd2 · e35cdd2
1 parent 045ee4a
commit e35cdd2
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diff --git a/Project.toml b/Project.toml
@@ -15,9 +15,12 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 MCMCChains = "c7f686f2-ff18-58e9-bc7b-31028e88f75d"
 Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
+ProgressBars = "49802e3a-d2f1-5c88-81d8-b72133a6f568"
 PyCall = "438e738f-606a-5dbb-bf0a-cddfbfd45ab0"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+RandomFeatures = "36c3bae2-c0c3-419d-b3b4-eebadd35c5e5"
 ScikitLearn = "3646fa90-6ef7-5e7e-9f22-8aca16db6324"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
 StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
 
@@ -32,8 +35,9 @@ GaussianProcesses = "0.12"
 MCMCChains = "4.14, 5, 6"
 PyCall = "1.93"
 ScikitLearn = "0.6, 0.7"
+RandomFeatures = "0.3"
 StatsBase = "0.33"
-julia = "1.6"
+julia = "1.6, 1.7, 1.8"
 
 [extras]
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"

diff --git a/docs/Project.toml b/docs/Project.toml
@@ -1,4 +1,3 @@
 [deps]
 CalibrateEmulateSample = "95e48a1f-0bec-4818-9538-3db4340308e3"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
-
diff --git a/docs/make.jl b/docs/make.jl
@@ -12,7 +12,11 @@ design = ["AbstractMCMC sampling API" => "API/AbstractMCMC.md"]
 
 api = [
     "CalibrateEmulateSample" => [
-        "Emulators" => ["General Emulator" => "API/Emulators.md", "Gaussian Process" => "API/GaussianProcess.md"],
+        "Emulators" => [
+            "General Interface" => "API/Emulators.md",
+            "Gaussian Process" => "API/GaussianProcess.md",
+            "Random Features" => "API/RandomFeatures.md",
+        ],
         "MarkovChainMonteCarlo" => "API/MarkovChainMonteCarlo.md",
         "Utilities" => "API/Utilities.md",
     ],

diff --git a/docs/src/API/GaussianProcess.md b/docs/src/API/GaussianProcess.md
@@ -8,6 +8,7 @@ CurrentModule = CalibrateEmulateSample.Emulators
 GaussianProcessesPackage
 PredictionType
 GaussianProcess
-build_models!
+build_models!(::GaussianProcess{GPJL}, ::PairedDataContainer{FT}) where {FT <: AbstractFloat}
 optimize_hyperparameters!(::GaussianProcess{GPJL})
+predict(::GaussianProcess{GPJL},  ::AbstractMatrix{FT}) where {FT <: AbstractFloat}
 ```
diff --git a/docs/src/API/RandomFeatures.md b/docs/src/API/RandomFeatures.md
@@ -0,0 +1,39 @@
+# RandomFeatures
+
+```@meta
+CurrentModule = CalibrateEmulateSample.Emulators
+```
+
+## Scalar interface
+
+```@docs
+ScalarRandomFeatureInterface
+ScalarRandomFeatureInterface(::Int,::Int)
+build_models!(::ScalarRandomFeatureInterface, ::PairedDataContainer{FT}) where {FT <: AbstractFloat}
+predict(::ScalarRandomFeatureInterface, ::M) where {M <: AbstractMatrix}
+```
+
+## Vector Interface
+
+```@docs
+VectorRandomFeatureInterface
+VectorRandomFeatureInterface(::Int, ::Int, ::Int)
+build_models!(::VectorRandomFeatureInterface, ::PairedDataContainer{FT}) where {FT <: AbstractFloat}
+predict(::VectorRandomFeatureInterface, ::M) where {M <: AbstractMatrix}
+```
+
+## Other utilities
+```@docs
+get_rfms
+get_fitted_features
+get_batch_sizes
+get_n_features
+get_input_dim
+get_output_dim
+get_rng
+get_diagonalize_input
+get_feature_decomposition
+get_optimizer_options
+optimize_hyperparameters!(::ScalarRandomFeatureInterface) 
+optimize_hyperparameters!(::VectorRandomFeatureInterface) 
+```
diff --git a/examples/Emulator/GaussianProcess/plot_GP.jl b/examples/Emulator/GaussianProcess/plot_GP.jl
@@ -68,9 +68,9 @@ if !isdir(output_directory)
 end
 
 #create the machine learning tools: Gaussian Process
-gppackage = GPJL()
+gppackage = SKLJL()
 pred_type = YType()
-gaussian_process = GaussianProcess(gppackage, noise_learn = true)
+gaussian_process = GaussianProcess(gppackage, noise_learn = false)
 
 # Generate training data (x-y pairs, where x ∈ ℝ ᵖ, y ∈ ℝ ᵈ)
 # x = [x1, x2]: inputs/predictors/features/parameters
@@ -92,7 +92,7 @@ gx[2, :] = g2x
 
 # Add noise η
 μ = zeros(d)
-Σ = 0.1 * [[0.8, 0.0] [0.0, 0.5]] # d x 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
@@ -182,9 +182,9 @@ println("GP trained")
 
 # Plot mean and variance of the predicted observables y1 and y2
 # For this, we generate test points on a x1-x2 grid.
-n_pts = 50
-x1 = range(0.0, stop = 2 * π, length = n_pts)
-x2 = range(0.0, stop = 2 * π, length = n_pts)
+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))

diff --git a/examples/Emulator/RandomFeature/Project.toml b/examples/Emulator/RandomFeature/Project.toml
@@ -0,0 +1,15 @@
+[deps]
+CalibrateEmulateSample = "95e48a1f-0bec-4818-9538-3db4340308e3"
+Conda = "8f4d0f93-b110-5947-807f-2305c1781a2d"
+Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
+FiniteDiff = "6a86dc24-6348-571c-b903-95158fe2bd41"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
+PyPlot = "d330b81b-6aea-500a-939a-2ce795aea3ee"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
+Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+
+[compat]
+FiniteDiff = "~2.10"
+julia = "~1.6"