From d5e1fff4a34db1029fe449b5f3efcf12c6cccb7a Mon Sep 17 00:00:00 2001 From: craftablescience Date: Mon, 9 Jun 2025 09:55:24 -0700 Subject: [PATCH] feat: add docs improvements for new users --- README.md | 17 + docs/extensions/_category_.json | 4 + docs/{guides => extensions}/bo-extension.md | 4 +- docs/getting-started/configuration.md | 49 + docs/getting-started/installation.md | 4 - docs/getting-started/tutorial_0.md | 14 +- docs/getting-started/tutorial_1.md | 248 -- docs/guides/api-usage.md | 118 - docs/guides/cli-usage.md | 318 --- docs/guides/color-by-environment.md | 4 +- ... => create-environments-and-interfaces.md} | 6 +- docs/guides/gui-usage.md | 111 +- docs/guides/history.md | 74 + docs/guides/templates.md | 74 + docs/intro.md | 2 +- package-lock.json | 2098 +++++++---------- package.json | 4 +- static/img/gui/highlight_algorithm.png | Bin 0 -> 157193 bytes static/img/gui/highlight_bottom_buttons.png | Bin 0 -> 212484 bytes static/img/gui/highlight_environment.png | Bin 0 -> 191979 bytes .../img/gui/highlight_history_navigator.png | Bin 0 -> 199760 bytes static/img/gui/highlight_load_template.png | Bin 0 -> 199984 bytes static/img/gui/highlight_metadata.png | Bin 0 -> 140144 bytes .../img/gui/highlight_new_optimization_1.png | Bin 0 -> 202533 bytes .../img/gui/highlight_new_optimization_2.png | Bin 0 -> 163402 bytes .../img/gui/highlight_plot_area_run_data.png | Bin 0 -> 196921 bytes static/img/gui/settings.png | Bin 36381 -> 38529 bytes 27 files changed, 1164 insertions(+), 1985 deletions(-) create mode 100644 docs/extensions/_category_.json rename docs/{guides => extensions}/bo-extension.md (98%) create mode 100644 docs/getting-started/configuration.md delete mode 100644 docs/getting-started/tutorial_1.md delete mode 100644 docs/guides/api-usage.md delete mode 100644 docs/guides/cli-usage.md rename docs/guides/{create-a-plugin.md => create-environments-and-interfaces.md} (99%) create mode 100644 docs/guides/history.md create mode 100644 docs/guides/templates.md create mode 100644 static/img/gui/highlight_algorithm.png create mode 100644 static/img/gui/highlight_bottom_buttons.png create mode 100644 static/img/gui/highlight_environment.png create mode 100644 static/img/gui/highlight_history_navigator.png create mode 100644 static/img/gui/highlight_load_template.png create mode 100644 static/img/gui/highlight_metadata.png create mode 100644 static/img/gui/highlight_new_optimization_1.png create mode 100644 static/img/gui/highlight_new_optimization_2.png create mode 100644 static/img/gui/highlight_plot_area_run_data.png diff --git a/README.md b/README.md index c9a6b6e..2d1954a 100644 --- a/README.md +++ b/README.md @@ -3,3 +3,20 @@ Any PR that merged into master branch of this repo will trigger an auto-deployment to the [Badger homepage](https://xopt-org.github.io/Badger/). This repo could be merged into [Badger core repo](https://github.com/xopt-org/Badger) soon. + +### Notes on creating highlighted images + +When making an image that highlights a particular portion of the GUI, this process was used for most images: + +1. Load the base image in GIMP - usually `static/img/gui/run_1.png`. +2. Make a rectangular selection around the UI element with antialiasing enabled and rounded corners set to 6px. +3. Invert the selection. (Select -> Invert) +4. Set saturation to 0.25. (Colors -> Saturation...) +5. Invert the selection. (Select -> Invert) +6. Create a stroke from the selection. (Edit -> Stroke Selection...) + - Use Line mode. + - Set the color to `#f83232`. + - Enable antialiasing. + - Set width to 6px. + - Use "Round" join style. +7. If adding numbers to denote different parts of the image, use Noto Sans Regular font. A size of 72px was used for most images. diff --git a/docs/extensions/_category_.json b/docs/extensions/_category_.json new file mode 100644 index 0000000..e938768 --- /dev/null +++ b/docs/extensions/_category_.json @@ -0,0 +1,4 @@ +{ + "label": "Extensions", + "position": 4 +} diff --git a/docs/guides/bo-extension.md b/docs/extensions/bo-extension.md similarity index 98% rename from docs/guides/bo-extension.md rename to docs/extensions/bo-extension.md index ba950dd..abc54c3 100644 --- a/docs/guides/bo-extension.md +++ b/docs/extensions/bo-extension.md @@ -1,8 +1,8 @@ --- -sidebar_position: 6 +sidebar_position: 1 --- -# BO Visualization Extension Documentation +# BO Visualization ## Overview diff --git a/docs/getting-started/configuration.md b/docs/getting-started/configuration.md new file mode 100644 index 0000000..89ad20d --- /dev/null +++ b/docs/getting-started/configuration.md @@ -0,0 +1,49 @@ +--- +sidebar_position: 2 +--- + +# Configuration + +## Property List + +#### Plugin Root + +The location of custom environments and interfaces. + +#### Templates Root + +The location where templates for new runs are stored. + +#### Logbook Root + +The location where Badger run logs are stored. In some environments (e.g. LCLS/LCLS-II) these logs are added to the machine elog. + +#### Archive Root + +The location where old runs are archived. Runs stored here will appear in the History Navigator panel in the GUI. + +--- + +## CLI + +If you would like to change some setting that you configured after `badger` has been run for the first time, you can do so with `badger config`. + +To list all the configuration properties: + +```bash +badger config +``` + +To set a property: + +```bash +badger config KEY +``` + +Where `KEY` is one of the keys in the configuration property list. + +## GUI + +The GUI includes a configuration panel as well, which may be more straightforward to use. Note that if some of these configuration properties are missing, the GUI will not launch, and you will need to set the missing property through the CLI interface. + +![Badger GUI settings popup](/img/gui/settings.png) diff --git a/docs/getting-started/installation.md b/docs/getting-started/installation.md index a216585..21bee15 100644 --- a/docs/getting-started/installation.md +++ b/docs/getting-started/installation.md @@ -6,10 +6,6 @@ sidebar_position: 1 Badger is essentially a python package. It's recommended to install and test it in a separate [python virtual environment](https://docs.python.org/3/library/venv.html) before using it in a production env. -## Requirements - -- Python version >= 3.9. Python 3.11+ is recommended - ## Install the Badger package Badger has a core package (`badger-opt`) that can be extended by a set of plugins. Badger core package is available on both [pip](https://pypi.org/project/badger-opt/) and [conda](https://anaconda.org/conda-forge/badger-opt). So you could install Badger with: diff --git a/docs/getting-started/tutorial_0.md b/docs/getting-started/tutorial_0.md index c6b8f22..144e484 100644 --- a/docs/getting-started/tutorial_0.md +++ b/docs/getting-started/tutorial_0.md @@ -1,8 +1,8 @@ --- -sidebar_position: 2 +sidebar_position: 3 --- -# Tutorial (GUI mode) +# Tutorial :::note Heads-up @@ -42,7 +42,9 @@ Run the following command in your terminal to launch the Badger GUI (assume you badger -g ``` -You should be able to see the main GUI like below: +Badger will ask if this is your first time using it. Either type a `y` and press Enter, or simply press Enter to fill out the default configuration values. + +When it finishes writing the default config, you should be able to see the main GUI like below: ![Badger GUI](/img/gui/blank.png) @@ -62,11 +64,11 @@ Congrats! You just run your very first routine in Badger! Now it's time to do some more serious stuff -- such as performing optimization on your own optimization problem. In order to do that, we need to create our own custom environment (and optionally, the corresponding interface). -First let's point Badger to a new folder where we would like to store our custom plugins, if necessary. At the bottom right of the Badger GUI window is a settings cog. When clicked it will open a popup to configure the Badger plugin root, logbook root, and archive root. The default folders should be reasonable, but these can be changed to suit your needs, if the defaults are difficult to access. +First let's point Badger to a new folder where we would like to store our custom plugins, if necessary. At the bottom right of the Badger GUI window is a settings cog. When clicked it will open a popup to configure the Badger plugin root, logbook root, and archive root. The default folders should be reasonable, but these can be changed to suit your needs, if the defaults are difficult to access. See [the configuration page](configuration) for more information. ![Badger GUI settings popup](/img/gui/settings.png) -Create a folder named `sphere_3d` in the environments folder beneath the plugin root. Then create the following files inside that `sphere_3d` folder: +In this example, we will be making an extension of the sphere_2d problem in 3 dimensions. Create a folder named `sphere_3d` in the environments folder beneath the plugin root. Then create the following files inside that `sphere_3d` folder: - The main python script: @@ -149,6 +151,6 @@ Press *Run* and stop whenever you feel right, you should see some optimization c ![Badger GUI after the second run](/img/gui/run_2.png) -Congrats! You have accomplished the Badger GUI tutorial! Hope that by this point you already have some feelings about what Badger is and how Badger works. Now you can continue and do the [CLI tutorial](tutorial_1) to get to know the other side of Badger, or you can jump directly to the [guides](../guides/create-a-plugin#create-an-environment-plugin) to adapt Badger to your own optimization problem, good luck! +Congrats! You have accomplished the Badger GUI tutorial! Hope that by this point you already have some feelings about what Badger is and how Badger works. You can continue to the guides to adapt Badger to your own optimization problem! Please reach out to the official mailing list or Slack channel by following the links in the footer if you have any questions or need advice. If you encounter a bug please make an issue on the [Badger GitHub repository](https://github.com/xopt-org/Badger). Good luck! [^generator]: Term in Xopt, means algorithm diff --git a/docs/getting-started/tutorial_1.md b/docs/getting-started/tutorial_1.md deleted file mode 100644 index ef1abf5..0000000 --- a/docs/getting-started/tutorial_1.md +++ /dev/null @@ -1,248 +0,0 @@ ---- -sidebar_position: 2 ---- - -# Tutorial (CLI mode) - -:::note Heads-up - -Make sure you have Badger [installed and setup](./installation). - -::: - -:::caution - -Badger's CLI mode is unsupported in this release. It may be supported again in the future. - -::: - -Let's discover **Badger in less than 5 minutes**. All of the following commands are assumed to be run in a terminal (Mac, Windows, and Linux are supported). - -First let's verify that Badger has been installed and configured correctly: - -```shell -badger -``` - -Which should give you something like: - -```shell title="output" -name: Badger the optimizer -version: 0.5.3 -plugin root: /root/badger/plugins -database root: /root/badger/db -logbook root: /root/badger/logbook -archive root: /root/badger/archived -extensions: - - xopt -``` - -## Run and save an optimization - -Create a yaml file under your `pwd` (where you would run an optimization with Badger) with the following content: - -```yaml title="config.yaml" -variables: - - x2 -objectives: - - c1 -``` - -To run and save an optimization, run: - -```shell -badger run -a silly -e TNK -c config.yaml -s helloworld -``` - -Badger will ask you to review the optimization routine: - -```shell title="output" -Please review the routine to be run: - -=== Optimization Routine === -name: mottled-sloth -algo: silly -env: TNK -algo_params: - dimension: 1 - max_iter: 42 -env_params: null -config: - variables: - - x2: 0 -> 3.14159 - objectives: - - c1: MINIMIZE - constraints: null - -Proceed ([y]/n)? -``` - -Hit return to confirm. Badger will print out a table of all the evaluated -solutions along the run: - -```shell {3,19} title="output" -| iter | c1 | x2 | ----------------------------------------- -| 1 | -1.094 | 0.07432 | -| 2 | 3.563 | 2.159 | -| 3 | 8.749 | 3.138 | -| 4 | 5.351 | 2.54 | -| 5 | 8.17 | 3.045 | -| 6 | 6.536 | 2.763 | -| 7 | 3.007 | 2.027 | -| 8 | -1.089 | 0.1063 | -| 9 | 4.127 | 2.286 | -| 10 | 3.519 | 2.149 | -| 11 | 6.647 | 2.783 | -| 12 | 1.074 | 1.474 | -| 13 | -0.8621 | 0.4878 | -| 14 | 3.821 | 2.218 | -| 15 | -0.9228 | 0.421 | -| 16 | 6.205 | 2.703 | -| 17 | -1.1 | 0.005409 | -| 18 | 8.224 | 3.054 | -| 19 | 7.584 | 2.947 | -| 20 | -0.8961 | 0.4515 | -| 21 | -1.093 | 0.08082 | -| 22 | 1.293 | 1.547 | -| 23 | 2.593 | 1.922 | -| 24 | 5.563 | 2.581 | -| 25 | 2.046 | 1.774 | -| 26 | 2.501 | 1.898 | -| 27 | -0.8853 | 0.4633 | -| 28 | -0.5459 | 0.7444 | -| 29 | -0.8881 | 0.4604 | -| 30 | -0.4806 | 0.787 | -| 31 | -1.1 | 0.01909 | -| 32 | 0.4855 | 1.259 | -| 33 | 0.8217 | 1.386 | -| 34 | 6.036 | 2.671 | -| 35 | -0.7649 | 0.5789 | -| 36 | 0.06972 | 1.082 | -| 37 | 7.325 | 2.903 | -| 38 | -0.7764 | 0.5689 | -| 39 | 6.042 | 2.673 | -| 40 | 5.008 | 2.471 | -| 41 | 4.274 | 2.318 | -| 42 | -0.8561 | 0.4939 | -======================================== -``` - -You would notice that the optimal solutions (in this case -optimal means minimal `c1`) at the evaluation time are highlighted. - -In the example above, we use the **silly** algorithm (which is just a random search algorithm) to optimize the **TNK** -environment, as shown in the reviewed routine. Environment **TNK** has 2 -variables and 5 observations: - -```yaml {7,8,10-14} title="TNK environment" -name: TNK -version: '0.1' -dependencies: - - numpy -params: null -variables: - - x1: 0 -> 3.14159 - - x2: 0 -> 3.14159 -observations: - - y1 - - y2 - - c1 - - c2 - - some_array -``` - -We specify in the `config.yaml` that we would like to tune varaible `x2`, and minimize observation `c1` of environment **TNK** as objective. The configuration that could reproduce the whole optimization setup is called a **routine** in Badger. A routine contains the information of the algorithm, the environment, and the config of the optimization (the variables, the objectives, and the constraints). - -We just saved the routine of the run as `helloworld`. Now you could view the routine again by: - -```shell -badger routine helloworld -``` - -## Rerun an optimization - -We can rerun a saved routine in Badger. Let's rerun the `helloworld` routine that we just saved: - -```shell -badger routine helloworld -r -``` - -Badger would behave exactly the same way as the first time you run the routine. - -## View the historical optimization data - -You can `cd` to the Badger archive root (the one you setup during the initial configurations) and view the historical optimization data. The file structure is a tree-like one, with year, year-month, year-month-day as the first 3 levels of branches, and the optimization runs as leaves: - -```shell {4} title="Badger archive root file structure" -|--2021 - |--2021-11 - |--2021-11-24 - |--BadgerOpt-2021-11-24-133007.yaml - |--BadgerOpt-2021-11-24-113241.yaml - |--... - |--... - |--... -|--... -``` - -The yaml data file contains the routine information and the solutions evaluated during the run. The content would look like this: - -```yaml title="BadgerOpt-2021-11-24-133007.yaml" -routine: - name: helloworld - algo: silly - env: TNK - algo_params: - dimension: 1 - max_iter: 10 - env_params: null - config: - variables: - - x2: - - 0.0 - - 3.1416 - objectives: - - c1: MINIMIZE - constraints: null -data: - timestamp: - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:06 - - 24-Nov-2021 13:30:07 - - 24-Nov-2021 13:30:07 - - 24-Nov-2021 13:30:07 - c1: - - 2.093905436806936 - - 2.6185501712620036 - - -0.8170601778601619 - - 7.869183841178197 - - -1.0945113202011 - - 0.514833333947652 - - -1.0331173238615994 - - 1.4523371516674013 - - 1.3610274948700156 - - -0.0042273815683477045 - x2: - - 1.78715008793524 - - 1.9283542649788197 - - 0.5319208795862764 - - 2.9948595695254556 - - 0.07408562477903413 - - 1.2707609271407632 - - 0.2586168520000207 - - 1.5976035652399507 - - 1.5687662333407153 - - 1.0467915830917118 -``` - -## Create a simple environment - -Now let's create a simple Badger environment and run optimization on it. - -**WIP** diff --git a/docs/guides/api-usage.md b/docs/guides/api-usage.md deleted file mode 100644 index 1fbe645..0000000 --- a/docs/guides/api-usage.md +++ /dev/null @@ -1,118 +0,0 @@ ---- -sidebar_position: 3 ---- - -# API Usage - -Badger can be imported as a regular python package, and you could use the plugins/utils that Badger offers in your own python script. - -:::note Heads-up - -Make sure you have Badger [installed and setup](../getting-started/installation). - -::: - -## Use an algorithm - -Badger has a `get_algo` API to get a specific algorithm. - -The following code gets an algorithm named `silly` (which is a random search algorithm) from Badger. - -```python -import numpy as np -from badger.factory import get_algo - -# Define a test evaluate function -def evaluate(X): - Y = np.linalg.norm(X, axis=1).reshape(-1, 1) # objectives - I = None # inequality constraints - E = None # equality constraints - - # Show the progress - print(Y) - - return Y, I, E - -# Get the silly algorithm from Badger -optimize, configs = get_algo('silly') - -# Optimize the test evaluate function -optimize(evaluate, configs['params']) -``` - -## Use an interface - -Badger has a `get_intf` API to get a specific interface. - -The following code gets an interface named `silly` and constructs an instance of the interface. - -```python -from badger.factory import get_intf - -# Get the silly interface from Badger -Interface, configs = get_intf('silly') -intf = Interface(configs['params']) - -# Test get/set channels -intf.get_value('c1') -# Output: 0 - -intf.set_value('c1', 1.0) -intf.get_value('c1') -# Output: 1.0 -``` - -## Use an environment - -Badger has a `get_env` API to get a specific environment. - -The following code gets and instantiates an environment named `silly` from Badger. Note that it uses the `silly` interface instance `intf` from the [last](#use-an-interface) section. - -```python -from badger.factory import get_env - -# Get the silly environment from Badger -Environment, configs = get_env('silly') -env = Environment(intf, configs['params']) - -# Investigate the silly env -env.list_vars() -# Output: ['q1', 'q2', 'q3', 'q4'] - -env.list_obses() -# Output: ['l1', 'l2'] - -env.get_var('q1') # q1 in env maps to c1 in intf -# Output: 1.0 - -env.get_obs('l2') # l2 norm of (q1, q2, q3, q4) -# Output: 1.0 - -env.set_var('q2', 1) -env.get_obs('l2') -# Output: 1.4142135623730951 -``` - -Now we can define an evaluate function based on the `silly` env, and use the `silly` algorithm from the [use an algorithm](#use-an-algorithm) section to optimize it. - -```python -# Define an evaluate function based on the env -def evaluate(X): - # Note that X is a 2D array - Y = [] - for x in X: - env.set_vars(['q1', 'q2', 'q3', 'q4'][:len(x)], x) - y = env.get_obs('l2') - Y.append(y) - Y = np.array(Y).reshape(-1, 1) - I = None - E = None - - # Show the progress - print(Y) - - return Y, I, E - -# Optimize the evaluate function with silly algorithm -optimize(evaluate, {'dimension': 4, 'max_iter': 42}) -``` diff --git a/docs/guides/cli-usage.md b/docs/guides/cli-usage.md deleted file mode 100644 index 74a37d6..0000000 --- a/docs/guides/cli-usage.md +++ /dev/null @@ -1,318 +0,0 @@ ---- -sidebar_position: 2 ---- - -# CLI Usage - -:::caution - -Badger's CLI mode is unsupported in this release. It may be supported again in the future. - -::: - -For all the implemented and planned CLI usage, please refer to [these slides](https://docs.google.com/presentation/d/1APlLgaRik2VPGL7FuxEUmwHvx6egTeIRaxBKGS1TnsE/edit#slide=id.ge68b2a5657_0_5). We'll highlight several common CLI use cases of Badger in the following sections. - -## Get help - -```bash -badger -h -``` - -Or [shoot me an email](mailto:zhezhang@slac.stanford.edu)! - -## Show metadata of Badger - -To show the version number and some other metadata such as plugin directory: - -```bash -badger -``` - -## Get information of the algorithms - -List all the available algorithms: - -```bash -badger algo -``` - -Get the configs of a specific algorithm: - -```bash -badger algo ALGO_NAME -``` - -You'll get something like: - -```yaml -name: silly -version: '0.1' -dependencies: - - numpy -params: - dimension: 1 - max_iter: 42 -``` - -Note that in order to use this plugin, you'll need to install the dependencies listed in the command output. This dependency installation will be handled automatically if the plugin was installed through the `badger install` command, but that command is not available yet (it is coming soon). - -The `params` part shows all the intrinsic parameters that can be tuned when doing optimization with this algorithm. - -## Get information of the environments - -List all the available environments: - -```bash -badger env -``` - -Get the configs of a specific environment: - -```bash -badger env ENV_NAME -``` - -The command will print out something like: - -```yaml -name: dumb -version: '0.1' -dependencies: - - numpy - - badger-opt -interface: - - silly -environments: - - silly - - naive -params: null -variables: - - q1: 0 -> 1 - - q2: 0 -> 1 - - q3: 0 -> 1 - - q4: 0 -> 1 - - s1: 0 -> 1 - - s2: 0 -> 1 -observations: - - l2 - - mean - - l2_x_mean -``` - -There are several important properties here: - -- `variables`: The tunable variables provided by this environment. You could choose a subset of the variables as the desicion variables for the optimization in the routine config. The allowed ranges (in this case, 0 to 1) are shown behind the corresponding variable names -- `observations`: The measurements provided by this environment. You could choose some observations as the objectives, and some other observations as the constraints in the routine config - -## Run and save an optimization - -```bash -badger run [-h] -a ALGO_NAME [-ap ALGO_PARAMS] -e ENV_NAME [-ep ENV_PARAMS] -c ROUTINE_CONFIG [-s [SAVE_NAME]] [-y] [-v [{0,1,2}]] -``` - -The `-ap` and `-ep` optional arguments, and the `-c` argument accept either a `.yaml` file path or a yaml string. The configs set to `-ap` and `-ep` optional arguments should be treated as "patch" on the default algorithm and environment parameters, respectively, which means that you only need to specify the paramters that you'd like to change on top of the default configs, rather than pass in a full config. The content of the `ROUTINE_CONFIG` (aka routine configs) should look like this: - -```yaml -variables: - - x1: [-1, 0.5] - - x2 -objectives: - - c1 - - y2: MINIMIZE -constraints: - - y1: - - GREATER_THAN - - 0 - - c2: - - LESS_THAN - - 0.5 -``` - -The `variables` and `objectives` properties are required, while the `constraints` property is optional. Just omit the `constraints` property if there are no constraints for your optimization problem. The names listed in `variables` should come from `variables` of the env specified by the `-e` argument, while the names listed in `objectives` and `constraints` should come from `observations` of that env. - -All optimization runs will be archived in the `$BADGER_ARCHIVE_ROOT` folder that you initially set up when running `badger` the first time. - -Several example routine configs can be found in the `examples` folder. - -Below are some example `badger run` commands. They are assumed to run under the parent directory of the `examples` folder (you'll need to clone the `examples` folder from this repo to your computer first). You could run them from any directory, just remember to change the routine config path accordingly. - -### A simplest run command - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -``` - -### Run without confirmation - -Badger will let you confirm the routine before running it. You could skip the confirmation by adding the `-y` option: - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -y -``` - -### Change verbose level - -By default, Badger will print out a table contains all the evaluated solutions along the optimization run (with the optimal ones highlighted), you could alter the default behavior by setting the `-v` option. - -The default verbose level 2 will print out all the solutions: - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -v 2 -``` - -The table would look like: - -``` -| iter | c1 | x2 | ----------------------------------------- -| 1 | 3.73 | 2.198 | -| 2 | -0.9861 | 0.3375 | -| 3 | 1.888 | 1.729 | -| 4 | 2.723 | 1.955 | -| 5 | -1.092 | 0.08923 | -| 6 | 1.357 | 1.568 | -| 7 | 4.559 | 2.379 | -| 8 | 8.757 | 3.14 | -| 9 | 2.957 | 2.014 | -| 10 | 0.1204 | 1.105 | -| 11 | 2.516 | 1.902 | -| 12 | -0.01194 | 1.043 | -| 13 | 7.953 | 3.009 | -| 14 | -1.095 | 0.07362 | -| 15 | -0.3229 | 0.8815 | -| 16 | -1.096 | 0.06666 | -| 17 | 2.662 | 1.94 | -| 18 | 6.987 | 2.844 | -| 19 | -0.9734 | 0.3558 | -| 20 | 3.694 | 2.19 | -| 21 | -1.032 | 0.2613 | -| 22 | 2.441 | 1.882 | -| 23 | 7.042 | 2.853 | -| 24 | 4.682 | 2.405 | -| 25 | 0.5964 | 1.302 | -| 26 | 0.3664 | 1.211 | -| 27 | 1.966 | 1.751 | -| 28 | 0.2181 | 1.148 | -| 29 | 7.954 | 3.009 | -| 30 | -0.8986 | 0.4488 | -| 31 | -0.7536 | 0.5885 | -| 32 | 3.602 | 2.168 | -| 33 | 0.5527 | 1.286 | -| 34 | -0.6969 | 0.6349 | -| 35 | -1.094 | 0.07974 | -| 36 | -0.8758 | 0.4735 | -| 37 | 5.995 | 2.664 | -| 38 | 3.638 | 2.177 | -| 39 | 2.489 | 1.895 | -| 40 | 0.8434 | 1.394 | -| 41 | 0.4919 | 1.262 | -| 42 | -0.4929 | 0.7792 | -======================================== -``` - -Verbose level 1 only prints out the optimal solutions along the run: - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -v 1 -``` - -The table would look like: - -``` -| iter | c1 | x2 | ----------------------------------------- -| 1 | 1.96 | 1.749 | -| 2 | -1.037 | 0.2518 | -| 18 | -1.1 | 0.01942 | -======================================== -``` - -Verbose level 0 turns off the printing feature completely: - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -v 0 -``` - -The table would not be printed. - -### Configure algorithm/environment parameters - -The following two commands show how to config parameters of the algorithm/environment. - -```bash -badger run -a silly -ap "dimension: 4" -e dumb -c examples/silly_dumb.yaml -``` - -```bash -badger run -a silly -ap "{dimension: 4, max_iter: 10}" -e dumb -c examples/silly_dumb.yaml -``` - -### Run with algorithms provided by extensions - -In order to run the following command, you'll need to [set up xopt](https://github.com/ChristopherMayes/Xopt#installing-xopt) on your computer (since the algorithms are provided by xopt). - -```bash -badger run -a cnsga -ap "max_generations: 10" -e TNK -c examples/cnsga_tnk.yaml -``` - -### Save a run - -To save a routine to database in `$BADGER_DB_ROOT`, just add the `-s [SAVE_NAME]` option. This command will run and save the routine with a randomly generated two-word name: - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -s -``` - -The following command will run the routine and save it as `test_routine`: - -```bash -badger run -a silly -e TNK -c examples/silly_tnk.yaml -s test_routine -``` - -## Rerun a saved optimization routine - -Say we have the routine `test_routine` saved. List all the saved routines by: - -```bash -badger routine -``` - -To get the details of some specific routine (say, `test_routine`): - -```bash -badger routine test_routine -``` - -To rerun it, do: - -```bash -badger routine test_routine -r -``` - -`badger routine` also supports the `-y` and `-v` options, as `badger run` does. - -## Configure Badger - -If you would like to change some setting that you configured during the first time you run `badger`, you could do so with `badger config`. - -List all the configurations: - -```bash -badger config -``` - -To config a property: - -```bash -badger config KEY -``` - -Where `KEY` is one of the keys in the configuration list. - -## Launch the Badger GUI - -Badger supports a GUI mode. You can launch the GUI by: - -``` -badger -g -``` diff --git a/docs/guides/color-by-environment.md b/docs/guides/color-by-environment.md index 3134d90..3ba3680 100644 --- a/docs/guides/color-by-environment.md +++ b/docs/guides/color-by-environment.md @@ -1,8 +1,8 @@ --- -sidebar_position: 5 +sidebar_position: 4 --- -# Color by environment +# Color by Environment You can color entries in the routine list and the border of the environment box in the routine editor based on the environment you choose. This is done by identifying colors with environments in a yaml file. diff --git a/docs/guides/create-a-plugin.md b/docs/guides/create-environments-and-interfaces.md similarity index 99% rename from docs/guides/create-a-plugin.md rename to docs/guides/create-environments-and-interfaces.md index eb36b57..cc8ae4e 100644 --- a/docs/guides/create-a-plugin.md +++ b/docs/guides/create-environments-and-interfaces.md @@ -1,13 +1,13 @@ --- -sidebar_position: 4 +sidebar_position: 5 --- -# Create a Plugin +# Create Environments and Interfaces Plugins have two types: -- Interface: class - Environment: class +- Interface: class Interface is the low-level layer between the machine/simulation and the environment that deals with the fundamental communications. It can be treated as an abstract of the underlying control system. Interface is optional **BUT** recommended! The pros of having an interface: diff --git a/docs/guides/gui-usage.md b/docs/guides/gui-usage.md index 087e0b2..0a16327 100644 --- a/docs/guides/gui-usage.md +++ b/docs/guides/gui-usage.md @@ -18,20 +18,41 @@ Within an environment, an optimization problem can be defined by selecting which ### Algorithm -The **Algorithm** section lets you select an algorithm to use for optimization. See “*Overview of Different Optimization Algorithms*” for a more detailed overview of different options. Common algorithms used at SLAC are expected improvement and nelder-mead. +![Badger GUI algorithm panel](/img/gui/highlight_algorithm.png) + +The **Algorithm** section lets you select an algorithm to use for optimization (1), as well as set the parameters of the selected algorithm (2). See “*Overview of Different Optimization Algorithms*” for a more detailed overview of different options. Common algorithms used at SLAC are expected improvement and nelder-mead. ### Metadata -**Metadata** includes a name and description for the optimization routine. +![Badger GUI metadata panel](/img/gui/highlight_metadata.png) + +**Metadata** includes a name (1) and description (2) for the optimization routine. Beneath the description there is also a button to save the current run configuration as a template. + +### Loading a Template + +![Badger GUI load template button](/img/gui/highlight_load_template.png) + +If there is already a template for the optimization you’d like to run, click the **Load Template** button at the upper left of the **Environment + VOCS** tab, and select the appropriate template. Make sure to check the environment parameters, variables and variable ranges, objectives, constraints/observables, and selected algorithm before running the optimization. See [the templates page](templates) for more information about templates. + +### History Navigator + +![Badger GUI history navigator panel](/img/gui/highlight_history_navigator.png) -### Loading a template +The History Navigator holds past runs, whose output can be loaded again with a single click on a given yaml file entry. Past runs are hierarchically organized by year, year and month, and year, month, and day, just like how they are organized in the Badger archive directory. -If there is already a template for the optimization you’d like to run, click the **Load Template** button at the upper left of the **Environment + VOCS** tab, and select the appropriate template. Make sure to check the environment parameters, variables and variable ranges, objectives, constraints/observables, and selected algorithm before running the optimization. +### Plot Area and Run Data + +![Badger GUI plot area and run data panel](/img/gui/highlight_plot_area_run_data.png) + +1. **Plot Area** is where run data is visualized as a line graph. +2. **Run Data** holds the raw data points which are fed into the plot. --- ## To Define a New Optimization Routine +![Badger GUI new optimization, upper page](/img/gui/highlight_new_optimization_1.png) + 1. **Start by selecting the target environment** from the **Environment** dropdown. Click the **Parameters** button to expand the available environment parameters. 2. **From the “Variables” table select devices to be optimized.** @@ -40,6 +61,8 @@ If there is already a template for the optimization you’d like to run, click t 3. **If the “Automatic” checkbox is checked, selecting a variable will automatically add a set of initial points.** By default, these will be the current value followed by three random points within a fraction of the variable bounds centered around the current value. If **Automatic** is not checked, or to adjust the initial points, you can use the **Add Current** and **Add Random** buttons to configure your own initial points. +![Badger GUI new optimization, lower page](/img/gui/highlight_new_optimization_2.png) + 4. **Select an objective from the “Objectives” table.** Make sure to select whether the objective should be maximized or minimized! Currently only single objective optimization is available, but multi-objective optimization will be supported in the future. @@ -56,83 +79,25 @@ If there is already a template for the optimization you’d like to run, click t ## Running the Optimization -Once the environment, variables, objectives, and algorithm (and any optional constraints and observables) have been defined, the optimization can be started by pressing the green **run** button at the lower center of the GUI. Badger will begin by measuring the objective at the initial points specified in the **Initial Points** table, and will then begin to optimize the selected variables using the chosen algorithm. When the scan is active, the green **run** button will turn into a red **stop** button. +![Badger GUI action buttons](/img/gui/highlight_bottom_buttons.png) + +Once the environment, variables, objectives, and algorithm (and any optional constraints and observables) have been defined, the optimization can be started by pressing the green **run** button (5) at the lower center of the GUI. Badger will begin by measuring the objective at the initial points specified in the **Initial Points** table, and will then begin to optimize the selected variables using the chosen algorithm. When the scan is active, the green **run** button will turn into a red **stop** button. + +- Once the scan has started, it can be paused/resumed using the **play/pause** button (4) to the left of the **run** button. +- To end the optimization run, press the red **stop** button (5). + +After ending the optimization, you may want to take some sort of action on the variables/devices being optimized. Depending on the algorithm selected, the last point/state sampled may not be the best that was found during the optimization run. To select the best configuration of variables that was measured, press the star button titled **Jump to Optimal** (6) to the right of the stop/start button. Alternatively, clicking any point in the optimization plot will highlight the variable values at that point in the scan. Once you’ve chosen the solution you’d like to implement, press **Dial in solution** (7) to set devices to the selected values. -- Once the scan has started, it can be paused/resumed using the **play/pause** button to the left of the **run** button. -- To end the optimization run, press the red **stop** button. +To reset all the variables to their values at the beginning of the scan, press the **Reset Environment** button (3). -After ending the optimization, you may want to take some sort of action on the variables/devices being optimized. Depending on the algorithm selected, the last point/state sampled may not be the best that was found during the optimization run. To select the best configuration of variables that was measured, press **Jump to Optimal** (star icon button) to the right of the stop/start button. Alternatively, clicking any point in the optimization plot will highlight the variable values at that point in the scan. Once you’ve chosen the solution you’d like to implement, press **Dial in solution** to set devices to the selected values. +Pressing the extensions button (8) will allow opening extension windows such as BOVisualizer and ParetoFrontViewer. -To reset all the variables to their values at the beginning of the scan, press the **Reset Environment** button. +Pressing the delete button (1) will delete the stored run data from the History Navigator panel and on disk. Pressing the log button (2) will save the current run's log to the configured logbook directory. While the optimization is running, the values of the variables, objectives, and (if selected) constraints and observables will be plotted in the plot section in the top right corner of the GUI. By default, the X-Axis displays the number of optimization iterations, and the Y-Axis for the variables plot is relative to each variable’s starting value. These options can be changed from the GUI via options in the top right corner, above the plots. --- -## Defining Template Files - -To save the current scan parameters as a template from the GUI, navigate to the **Metadata** tab. Click **Save as Template**, and enter an appropriate filename ending in “.yaml”. This will save the Environment, VOCS, Algorithm, and Metadata currently displayed on the GUI to a YAML file, including environment and algorithm parameters and relative variable ranges, and the configuration of initial points. - -Templates can also be directly saved or edited as YAML files, with the following format: - -```yaml -name: '' # name of template -description: '' # description of template -environment: - name: '' # environment name - params: {} # environment parameters, depend on environment -generator: - name: # generator name - # params will depend on generator -vocs: # XOPT VOCS - constants: {} # {constant_name: value} - constraints: {} # {constraint_name: [GREATER_THAN or LESS_THAN, value]} - objectives: {} # {objective_name: MINIMIZE or MAXIMIZE} - observables: [] # list of observable names - variables: {} # {variable_name: [lower_bound, upper_bound]} - - # Note that the variable upper and lower bound should be - # the absolute variable range limits, not the limit of the - # optimization. The range of the optimization is set based - # on vrange_limit_options for each variable, either as a - # fraction of the full range or ± a fraction of the - # current value. - -vrange_limit_options: {} - - # for each variable: - # variable: {limit_option_idx: 0 or 1, ratio_curr: 0.1, ratio_full: 0.1} - # For example: - # QUAD:LTUH:620:BCTRL: - # limit_option_idx: 0 - # ratio_curr: 0.1 - # ratio_full: 0.1 - # Note that ratio_curr is the ratio with respect to the current value - # and ratio_full is the ratio with respect to the full variable range. - # limit_option_idx 0 will use ratio_curr, 1 is ratio_full - -relative_to_current: true # true or false. -initial_point_actions: [{}] # list of dictionaries - - # Will be read sequentially. - # For example, the two most common options would look like: - # - # - type: add_curr # will add the current value of selected vars - # - config: - # fraction: 0.1 - # method: 0 - # n_points: 3 - # type: add_rand - # - # will add three random points for each variable, selected - # from within 0.1*(the vrange limit ratio for that - # variable) around the current value – i.e. sample n random - # points from within a subset of the scan range - -critical_constraint_names: [] # list of constraints (from VOCS) to be marked as ‘critical’ -badger_version: # optional but helpful -xopt_version: # optional but helpful -``` ## Overview of Different Optimization Algorithms ### Nelder-Mead diff --git a/docs/guides/history.md b/docs/guides/history.md new file mode 100644 index 0000000..de9ffa9 --- /dev/null +++ b/docs/guides/history.md @@ -0,0 +1,74 @@ +--- +sidebar_position: 2 +--- + +# History + +Badger stores the output of every run in a collection of yaml files. You can `cd` to the Badger archive root and view the historical optimization data. The file structure is a tree-like one, with year, year-month, year-month-day as the first 3 levels of branches, and the optimization runs as leaves: + +```shell {4} title="Badger archive root file structure" +|--2021 + |--2021-11 + |--2021-11-24 + |--BadgerOpt-2021-11-24-133007.yaml + |--BadgerOpt-2021-11-24-113241.yaml + |--... + |--... + |--... +|--... +``` + +The yaml data file contains the routine information and the solutions evaluated during the run. The content would look like this: + +```yaml title="BadgerOpt-2021-11-24-133007.yaml" +routine: + name: helloworld + algo: silly + env: TNK + algo_params: + dimension: 1 + max_iter: 10 + env_params: null + config: + variables: + - x2: + - 0.0 + - 3.1416 + objectives: + - c1: MINIMIZE + constraints: null +data: + timestamp: + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:06 + - 24-Nov-2021 13:30:07 + - 24-Nov-2021 13:30:07 + - 24-Nov-2021 13:30:07 + c1: + - 2.093905436806936 + - 2.6185501712620036 + - -0.8170601778601619 + - 7.869183841178197 + - -1.0945113202011 + - 0.514833333947652 + - -1.0331173238615994 + - 1.4523371516674013 + - 1.3610274948700156 + - -0.0042273815683477045 + x2: + - 1.78715008793524 + - 1.9283542649788197 + - 0.5319208795862764 + - 2.9948595695254556 + - 0.07408562477903413 + - 1.2707609271407632 + - 0.2586168520000207 + - 1.5976035652399507 + - 1.5687662333407153 + - 1.0467915830917118 +``` diff --git a/docs/guides/templates.md b/docs/guides/templates.md new file mode 100644 index 0000000..0321494 --- /dev/null +++ b/docs/guides/templates.md @@ -0,0 +1,74 @@ +--- +sidebar_position: 3 +--- + +# Templates + +## Loading a Template + +If there is already a template for the optimization you’d like to run, click the **Load Template** button at the upper left of the **Environment + VOCS** tab, and select the appropriate template. Make sure to check the environment parameters, variables and variable ranges, objectives, constraints/observables, and selected algorithm before running the optimization. + +## Defining Template Files + +To save the current scan parameters as a template from the GUI, navigate to the **Metadata** tab. Click **Save as Template**, and enter an appropriate filename ending in “.yaml”. This will save the Environment, VOCS, Algorithm, and Metadata currently displayed on the GUI to a YAML file, including environment and algorithm parameters and relative variable ranges, and the configuration of initial points. + +Templates can also be directly saved or edited as YAML files, with the following format: + +```yaml +name: '' # name of template +description: '' # description of template +environment: + name: '' # environment name + params: {} # environment parameters, depend on environment +generator: + name: # generator name + # params will depend on generator +vocs: # XOPT VOCS + constants: {} # {constant_name: value} + constraints: {} # {constraint_name: [GREATER_THAN or LESS_THAN, value]} + objectives: {} # {objective_name: MINIMIZE or MAXIMIZE} + observables: [] # list of observable names + variables: {} # {variable_name: [lower_bound, upper_bound]} + + # Note that the variable upper and lower bound should be + # the absolute variable range limits, not the limit of the + # optimization. The range of the optimization is set based + # on vrange_limit_options for each variable, either as a + # fraction of the full range or ± a fraction of the + # current value. + +vrange_limit_options: {} + + # for each variable: + # variable: {limit_option_idx: 0 or 1, ratio_curr: 0.1, ratio_full: 0.1} + # For example: + # QUAD:LTUH:620:BCTRL: + # limit_option_idx: 0 + # ratio_curr: 0.1 + # ratio_full: 0.1 + # Note that ratio_curr is the ratio with respect to the current value + # and ratio_full is the ratio with respect to the full variable range. + # limit_option_idx 0 will use ratio_curr, 1 is ratio_full + +relative_to_current: true # true or false. +initial_point_actions: [{}] # list of dictionaries + + # Will be read sequentially. + # For example, the two most common options would look like: + # + # - type: add_curr # will add the current value of selected vars + # - config: + # fraction: 0.1 + # method: 0 + # n_points: 3 + # type: add_rand + # + # will add three random points for each variable, selected + # from within 0.1*(the vrange limit ratio for that + # variable) around the current value – i.e. sample n random + # points from within a subset of the scan range + +critical_constraint_names: [] # list of constraints (from VOCS) to be marked as ‘critical’ +badger_version: # optional but helpful +xopt_version: # optional but helpful +``` diff --git a/docs/intro.md b/docs/intro.md index 24c95a4..894dfe5 100644 --- a/docs/intro.md +++ b/docs/intro.md @@ -112,7 +112,7 @@ Environments and interfaces are all plugins in Badger.[^algo] A plugin in Badger |--... ``` -The role/feature of each file will be discussed in details later in the [create a plugin](guides/create-a-plugin) section. +The role/feature of each file will be discussed in details later in the [create environments and interfaces](guides/create-environments-and-interfaces) section. :::tip diff --git a/package-lock.json b/package-lock.json index 387591a..a6be4a9 100644 --- a/package-lock.json +++ b/package-lock.json @@ -8,8 +8,8 @@ "name": "badger-home", "version": "0.0.0", "dependencies": { - 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