Tutorial (CLI mode)
Make sure you have Badger installed and setup.
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:
badger
Which should give you something like:
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:
variables:
- x2
objectives:
- c1
To run and save an optimization, run:
badger run -a silly -e TNK -c config.yaml -s helloworld
Badger will ask you to review the optimization routine:
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:
| 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:
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:
badger routine helloworld
Rerun an optimization
We can rerun a saved routine in Badger. Let's rerun the helloworld routine that we just saved:
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:
|--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:
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