Overview

This repository is an effort to standardize the interface of the generators in optimization libraries such as Xopt, optimas, libEnsemble, rsopt.

The objective of this effort is for these different libraries to be able to use each other's generators with little effort.

Example: using Xopt generators in optimas

Definitions

Generator:

A generator is an object that recommends points to be evaluated in an optimization. It can also receive data (evaluations from past or on-going optimization), which helps it make more informed recommendations.

Note: The generator does not orchestrate the overall optimization (e.g. dispatch evaluations, etc.). As such, it is distinct from libEnsemble's gen_f function, and is not itself "workflow" software.

*Examples:
- Xopt: here is the generator for the Nelder-Mead method. All Xopt generators implement the methods generate (i.e. make recommendations) and add_data (i.e. receive data).
- optimas: here is the base class for all generators. It implements the methods ask (i.e. make recommendations) and tell (i.e. receive data).

Standardization

Each type of generator (e.g., Nelder-Nead, different flavors of GA, BO, etc.) will be a Python class that defines the following methods:

Constructor: __init__(self, *args, **kwargs):

The constructor will include variable positional and keyword arguments to accommodate the different options that each type of generator has.
ask(num_points: Optional[int] = None) -> List[Dict]:

Returns set of points in the input space, to be evaluated next. Each element of the list is a separate point. Keys of the dictionary correspond to the name of each input variable.
- When num_points is not passed: the generator decides how many points to return. Different generators will return different number of points, by default. For instance, the simplex would return 1 or 3 points. A genetic algorithm could return the whole population. Batched Bayesian optimization would return the batch size (i.e., number of points that can be processed in parallel), which would be specified in the constructor.
- When it is passed: the generator should return exactly this number of points, or raise a error ValueError if it is unable to. If the user is flexible about the number of points, it should simply not pass num_points.
Examples:
```
>>> generator.ask(100)  # too many points
ValueError
```
```
>>> generator.ask()
[{"x": 1.2, "y": 0.8}, {"x": -0.2, "y": 0.4}, {"x": 4.3, "y": -0.1}]
```
tell(points: List[Dict]):

Feeds data (past evaluations) to the generator. Each element of the list is a separate point. Keys of the dictionary correspond to the name of each input and output variable.

Example:
```
>>> point = generator.ask(1)
>>> point
[{"x": 1, "y": 1}]
>>> point["f"] = objective(point)
>>> point
[{"x": 1, "y": 1, "f": 2}]
>>> generator.tell(point)
```

campa-consortium/generator_standard

Overview

Definitions

Standardization