diff --git a/.dev/Project.toml b/.dev/Project.toml index d86279dc7..d03b31452 100644 --- a/.dev/Project.toml +++ b/.dev/Project.toml @@ -2,4 +2,4 @@ JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899" [compat] -JuliaFormatter = "0.3" +JuliaFormatter = "0.22" diff --git a/Manifest.toml b/Manifest.toml deleted file mode 100644 index 9cb01ed15..000000000 --- a/Manifest.toml +++ /dev/null @@ -1,890 +0,0 @@ -# This file is machine-generated - editing it directly is not advised - -[[AMD]] -deps = ["Libdl", "LinearAlgebra", "SparseArrays", "Test"] -git-tree-sha1 = "fc66ffc5cff568936649445f58a55b81eaf9592c" -uuid = "14f7f29c-3bd6-536c-9a0b-7339e30b5a3e" -version = "0.4.0" - -[[AbstractFFTs]] -deps = ["ChainRulesCore", "LinearAlgebra"] -git-tree-sha1 = "6f1d9bc1c08f9f4a8fa92e3ea3cb50153a1b40d4" -uuid = "621f4979-c628-5d54-868e-fcf4e3e8185c" -version = "1.1.0" - -[[AbstractMCMC]] -deps = ["BangBang", "ConsoleProgressMonitor", "Distributed", "Logging", "LoggingExtras", "ProgressLogging", "Random", "StatsBase", "TerminalLoggers", "Transducers"] -git-tree-sha1 = "5dfb2514d115b449127f29eb7b3272c5528d396e" -uuid = "80f14c24-f653-4e6a-9b94-39d6b0f70001" -version = "3.3.1" - 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editing it directly is not advised - -julia_version = "1.7.1" -manifest_format = "2.0" - -[[deps.ANSIColoredPrinters]] -git-tree-sha1 = "574baf8110975760d391c710b6341da1afa48d8c" -uuid = "a4c015fc-c6ff-483c-b24f-f7ea428134e9" -version = "0.0.1" - -[[deps.Base64]] -uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" - -[[deps.Dates]] -deps = ["Printf"] -uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" - -[[deps.DocStringExtensions]] -deps = ["LibGit2"] -git-tree-sha1 = "b19534d1895d702889b219c382a6e18010797f0b" -uuid = "ffbed154-4ef7-542d-bbb7-c09d3a79fcae" -version = "0.8.6" - -[[deps.Documenter]] -deps = ["ANSIColoredPrinters", "Base64", "Dates", "DocStringExtensions", "IOCapture", "InteractiveUtils", "JSON", "LibGit2", "Logging", "Markdown", "REPL", "Test", "Unicode"] -git-tree-sha1 = "f425293f7e0acaf9144de6d731772de156676233" -uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4" -version = "0.27.10" - -[[deps.IOCapture]] -deps = ["Logging", "Random"] -git-tree-sha1 = "f7be53659ab06ddc986428d3a9dcc95f6fa6705a" -uuid = "b5f81e59-6552-4d32-b1f0-c071b021bf89" -version = "0.2.2" - 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-[[deps.Random]] -deps = ["SHA", "Serialization"] -uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" - -[[deps.SHA]] -uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" - -[[deps.Serialization]] -uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" - -[[deps.Sockets]] -uuid = "6462fe0b-24de-5631-8697-dd941f90decc" - -[[deps.Test]] -deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] -uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" - -[[deps.Unicode]] -uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" diff --git a/docs/Project.toml b/docs/Project.toml index dfa65cd10..5e2511daf 100644 --- a/docs/Project.toml +++ b/docs/Project.toml @@ -1,2 +1,3 @@ [deps] Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4" + diff --git a/docs/make.jl b/docs/make.jl index 70c8452c1..aab951a80 100644 --- a/docs/make.jl +++ b/docs/make.jl @@ -22,6 +22,7 @@ api = [ pages = [ "Home" => "index.md", "Installation instructions" => "installation_instructions.md", + "Contributing" => "contributing.md", "Examples" => examples, "Package Design" => design, "API" => api, diff --git a/docs/src/assets/logo.svg b/docs/src/assets/logo.svg new file mode 100644 index 000000000..5bf8ae397 --- /dev/null +++ b/docs/src/assets/logo.svg @@ -0,0 +1,71 @@ + + + + + + + + image/svg+xml + + + + + + + + + + + diff --git a/docs/src/contributing.md b/docs/src/contributing.md new file mode 100644 index 000000000..7c1c4e8b5 --- /dev/null +++ b/docs/src/contributing.md @@ -0,0 +1,163 @@ +# Contributing + +Thank you for considering contributing to `CalibrateEmulateSample`! We encourage opening issues and pull requests (PRs). + +## What to contribute? + +- The easiest way to contribute is by using `CalibrateEmulateSample`, identifying + problems and opening issues; + +- You can try to tackle an existing [issue](https://github.com/CliMA/CalibrateEmulateSample.jl/issues). It is best to outline your proposed solution in the issue thread before implementing it in a PR; + +- Write an example or tutorial. It is likely that other users may find your use of `CalibrateEmulateSample` insightful; + +- Improve documentation or comments if you found something hard to use; + +- Implement a new feature if you need it. We strongly encourage opening an issue to make sure the administrators are on board before opening a PR with an unsolicited feature addition. + +## Using `git` + +If you are unfamiliar with `git` and version control, the following guides +will be helpful: + +- [Atlassian (bitbucket) `git` + tutorials](https://www.atlassian.com/git/tutorials). A set of tips and tricks + for getting started with `git`. +- [GitHub's `git` tutorials](https://try.github.io/). A set of resources from + GitHub to learn `git`. + +### Forks and branches + +Create your own fork of `CalibrateEmulateSample` [on +GitHub](https://github.com/CliMA/CalibrateEmulateSample.jl) and check out your copy: + +``` +$ git clone https://github.com//CalibrateEmulateSample.jl.git +$ cd CalibrateEmulateSample.jl +``` + +Now you have access to your fork of `CalibrateEmulateSample` through `origin`. Create a branch for your feature; this will hold your contribution: + +``` +$ git checkout -b +``` + +#### Some useful tips + +- When you start working on a new feature branch, make sure you start from + main by running: `git checkout main` and `git pull`. + +- Create a new branch from main by using `git checkout -b `. + +### Develop your feature + +Make sure you add tests for your code in `test/` and appropriate documentation in the code and/or +in `docs/`. Before committing your changes, you can verify their behavior by running the tests, the examples, and building the documentation [locally](https://clima.github.io/CalibrateEmulateSample.jl/dev/installation_instructions/). In addition, make sure your feature follows the formatting guidelines by running +``` +julia --project=.dev .dev/climaformat.jl . +``` +from the `CalibrateEmulateSample.jl` directory. + +### Squash and rebase + +When your PR is ready for review, clean up your commit history by squashing +and make sure your code is current with `CalibrateEmulateSample.jl` main by rebasing. The general rule is that a PR should contain a single commit with a descriptive message. + +To make sure you are up to date with main, you can use the following workflow: + +``` +$ git checkout main +$ git pull +$ git checkout +$ git rebase main +``` +This may create conflicts with the local branch. The conflicted files will be outlined by git. To resolve conflicts, +we have to manually edit the files (e.g. with vim). The conflicts will appear between >>>>, ===== and <<<<<. +We need to delete these lines and pick what version we want to keep. + +To squash your commits, you can use the following command: + +``` +$ git rebase -i HEAD~n +``` + +where `n` is the number of commits you need to squash into one. Then, follow the instructions in the terminal. For example, to squash 4 commits: +``` +$ git rebase -i HEAD~4 +``` +will open the following file in (typically) vim: + +``` + pick 01d1124 + pick 6340aaa + pick ebfd367 + pick 30e0ccb + + # Rebase 60709da..30e0ccb onto 60709da + # + # Commands: + # p, pick = use commit + # e, edit = use commit, but stop for amending + # s, squash = use commit, but meld into previous commit + # + # If you remove a line here THAT COMMIT WILL BE LOST. + # However, if you remove everything, the rebase will be aborted. +## +``` + +We want to keep the first commit and squash the last 3. We do so by changing the last three commits to `squash` and then do `:wq` on vim. + +``` + pick 01d1124 + squash 6340aaa + squash ebfd367 + squash 30e0ccb + + # Rebase 60709da..30e0ccb onto 60709da + # + # Commands: + # p, pick = use commit + # e, edit = use commit, but stop for amending + # s, squash = use commit, but meld into previous commit + # + # If you remove a line here THAT COMMIT WILL BE LOST. + # However, if you remove everything, the rebase will be aborted. +``` + +Then in the next screen that appears, we can just delete all messages that +we do not want to show in the commit. After this is done and we are back to +the console, we have to force push. We need to force push because we rewrote +the local commit history. + +``` +$ git push -u origin --force +``` + +You can find more information about squashing [here](https://github.com/edx/edx-platform/wiki/How-to-Rebase-a-Pull-Request#squash-your-changes). + +### Unit testing + +Currently a number of checks are run per commit for a given PR. + +- `JuliaFormatter` checks if the PR is formatted with `.dev/climaformat.jl`. +- `Documentation` rebuilds the documentation for the PR and checks if the docs + are consistent and generate valid output. +- `Unit Tests` run subsets of the unit tests defined in `tests/`, using `Pkg.test()`. + The tests are run in parallel to ensure that they finish in a reasonable time. + The tests only run the latest commit for a PR, branch and will kill any stale jobs on push. + These tests are only run on linux (Ubuntu LTS). + +Unit tests are run against every new commit for a given PR, +the status of the unit-tests are not checked during the merge +process but act as a sanity check for developers and reviewers. +Depending on the content changed in the PR, some CI checks that +are not necessary will be skipped. For example doc only changes +do not require the unit tests to be run. + +### The merge process + +We use [`bors`](https://bors.tech/) to manage merging PR's in the the `CalibrateEmulateSample` repo. +If you're a collaborator and have the necessary permissions, you can type +`bors try` in a comment on a PR to have integration test suite run on that +PR, or `bors r+` to try and merge the code. Bors ensures that all integration tests +for a given PR always pass before merging into `main`. The integration tests currently run example cases in `examples/`. Any breaking changes will need to also update the `examples/`, else bors will fail. diff --git a/docs/src/index.md b/docs/src/index.md index 113ef1667..b5ef9b04e 100644 --- a/docs/src/index.md +++ b/docs/src/index.md @@ -1,33 +1,51 @@ # CalibrateEmulateSample.jl -`CalibrateEmulateSample.jl` solves parameter estimation problems using (approximate) Bayesian inversion. It is designed for problems that require running a computer model that is expensive to evaluate, but can also be used for simple models. +`CalibrateEmulateSample.jl` solves parameter estimation problems using accelerated (and approximate) Bayesian inversion. -The computer model is supplied by the user – it is a forward model, i.e., it takes certain parameters and produces data that can then be compared with the actual observations. We can think of that model as a parameter-to-data map ``G(u): \mathbb{R}^p \rightarrow \mathbb{R}^d``. For example, ``G`` could be a global climate model or a model that predicts the motion of a robot arm. +The framework can be applied currently to learn: +- the joint distribution for a moderate numbers of parameters (<40), +- it is not inherently restricted to unimodal distributions. + +it can be used with computer models that: +- can be noisy or chaotic, +- are non-differentiable, +- can only be treated as black-box (interfaced only with parameter files). + +The computer model is supplied by the user, as a parameter-to-data map ``G(u): \mathbb{R}^p \rightarrow \mathbb{R}^d``. For example, ``G`` could be a map from any given parameter configuration ``u`` to a collection of statistics of a dynamical system trajectory. The data produced by the forward model are compared to observations $y$, which are assumed to be corrupted by additive noise ``\eta``, such that ```math -\begin{equation} -y = G(u) + \eta -\end{equation}, +y = G(u) + \eta, ``` where the noise ``\eta`` is drawn from a d-dimensional Gaussian with distribution ``\mathcal{N}(0, \Gamma_y)``. -Given knowledge of the observations ``y``, the forward model ``G(u): \mathbb{R}^p \rightarrow \mathbb{R}^d``, and some information about the noise level such as its size or distribution (but not its value), the inverse problem we want to solve is to find the unknown parameters ``u``. +### The inverse problem + +Given an observation ``y``, the computer model ``G``, the observational noise ``\Gamma_y``, and some broad prior information on ``u``, we return the joint distribution of a data-informed distribution for "``u`` given ``y``". -As the name suggests, `CalibrateEmulateSample.jl` breaks this problem into a sequence of three steps: calibration, emulation, and sampling. -A comprehensive treatment of the calibrate-emulate-sample approach to Bayesian inverse problems can be found in [Cleary et al., 2020](https://arxiv.org/pdf/2001.03689.pdf). +As the name suggests, `CalibrateEmulateSample.jl` breaks this problem into a sequence of three steps: calibration, emulation, and sampling. A comprehensive treatment of the calibrate-emulate-sample approach to Bayesian inverse problems can be found in [Cleary et al., 2020](https://arxiv.org/pdf/2001.03689.pdf). + +### The three steps of the algorithm: -In a one-sentence summary, the **calibrate** step of the algorithm consists of an Ensemble Kalman inversion that is used to find good training points for a Gaussian process regression, which in turn is used as a surrogate (**emulator**) of the original forward model ``G`` in the subsequent Markov chain Monte Carlo **sampling** of the posterior distributions of the unknown parameters. +The **calibrate** step of the algorithm consists of an application of [Ensemble Kalman Processes](https://github.com/CliMA/EnsembleKalmanProcesses.jl), that generates input-output pairs in high density around an optimal parameter ``u^*``. This ``u^*`` will be near a mode of the posterior distribution (Note: This the only time we interface with the forward model ``G``). +The **emulate** step takes these pairs and trains a statistical surrogate model (Gaussian process), emulating the forward map ``G``. + +The **sample** step uses this surrogate in place of ``G`` in a sampling method (Markov chain Monte Carlo) to sample the posterior distribution of ``u``. `CalibrateEmulateSample.jl` contains the following modules: -Module | Purpose ---------------------------------------------|-------------------------------------------------------- -EnsembleKalmanProcesses.jl | Calibrate – Ensemble Kalman inversion -GaussianProcessEmulator.jl | Emulate – Gaussian process regression -MarkovChainMonteCarlo.jl | Sample – Markov chain Monte Carlo -Observations.jl | Structure to hold observations -Utilities.jl | Helper functions +Module | Purpose +-----------------------------|-------------------------------------------------------- +CalibrateEmulateSample.jl | Pulls in the [Ensemble Kalman Processes](https://github.com/CliMA/EnsembleKalmanProcesses.jl) package +Emulator.jl | Emulate: Modular template for emulators +GaussianProcess.jl | - A Gaussian process emulator +MarkovChainMonteCarlo.jl | Sample: Modular template for MCMC +Utilities.jl | Helper functions **The best way to get started is to have a look at the examples!** + +## Authors + +`CalibrateEmulateSample.jl` is being developed by the [Climate Modeling +Alliance](https://clima.caltech.edu). \ No newline at end of file diff --git a/src/Emulator.jl b/src/Emulator.jl index 4a3c1aa56..831da66c4 100644 --- a/src/Emulator.jl +++ b/src/Emulator.jl @@ -175,7 +175,7 @@ function predict( N_samples = size(new_inputs, 2) size(new_inputs, 1) == input_dim || - throw(ArgumentError("GP object and input observations do not have consistent dimensions")) + throw(ArgumentError("GP object and input observations do not have consistent dimensions")) # [1.] normalize normalized_new_inputs = normalize(emulator, new_inputs) diff --git a/src/MarkovChainMonteCarlo.jl b/src/MarkovChainMonteCarlo.jl index d8731d5da..ce1f9b954 100644 --- a/src/MarkovChainMonteCarlo.jl +++ b/src/MarkovChainMonteCarlo.jl @@ -568,8 +568,16 @@ function get_posterior(mcmc::MCMCWrapper, chain::MCMCChains.Chains) # Cast data in chain to a ParameterDistribution object. Data layout in Chain is an # (N_samples x n_params x n_chains) AxisArray, so samples are in rows. p_chain = Array(Chains(chain, :parameters)) # discard internal/diagnostic data - posterior_samples = [Samples(p_chain[:, slice, 1], params_are_columns = false) for slice in p_slices] - posterior_distribution = ParameterDistribution(posterior_samples, p_constraints, p_names) + p_samples = [Samples(p_chain[:, slice, 1], params_are_columns = false) for slice in p_slices] + + # distributions created as atoms and pieced together + posterior_distribution = combine_distributions([ + ParameterDistribution(ps, pc, pn) for (ps, pc, pn) in zip(p_samples, p_constraints, p_names) + ]) + + + + # posterior_distribution = ParameterDistribution(posterior_samples, p_constraints, p_names) return posterior_distribution end diff --git a/test/GaussianProcess/runtests.jl b/test/GaussianProcess/runtests.jl index f4b176f4e..8a584457c 100644 --- a/test/GaussianProcess/runtests.jl +++ b/test/GaussianProcess/runtests.jl @@ -132,7 +132,7 @@ using CalibrateEmulateSample.DataContainers pred_type = YType() # Generate training data - m = 80 # number of training points + m = 100 # number of training points p = 2 # input dim d = 2 # output dim @@ -147,7 +147,7 @@ using CalibrateEmulateSample.DataContainers # Add noise η μ = zeros(d) - Σ = 0.1 * [[0.8, 0.2] [0.2, 0.5]] # d x d + Σ = 0.05 * [[0.5, 0.2] [0.2, 0.5]] # d x d noise_samples = rand(MvNormal(μ, Σ), m) # y = G(x) + η @@ -177,10 +177,10 @@ using CalibrateEmulateSample.DataContainers μ4, σ4² = Emulators.predict(em4, new_inputs, transform_to_real = true) - @test μ4[:, 1] ≈ [1.0, -1.0] atol = 0.25 - @test μ4[:, 2] ≈ [0.0, 2.0] atol = 0.25 - @test μ4[:, 3] ≈ [0.0, 0.0] atol = 0.25 - @test μ4[:, 4] ≈ [0.0, -2.0] atol = 0.25 + @test μ4[:, 1] ≈ [1.0, -1.0] atol = 0.3 + @test μ4[:, 2] ≈ [0.0, 2.0] atol = 0.3 + @test μ4[:, 3] ≈ [0.0, 0.0] atol = 0.3 + @test μ4[:, 4] ≈ [0.0, -2.0] atol = 0.3 @test length(σ4²) == size(new_inputs, 2) @test size(σ4²[1]) == (d, d)