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Optimize with SigOpt with this standalone SigOpt client driver.

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# PROJECT NOT UNDER ACTIVE MANAGEMENT

This project will no longer be maintained by Intel.
Intel has ceased development and contributions including, but not limited to, maintenance, bug fixes, new releases, or updates, to this project.
Intel no longer accepts patches to this project.
If you have an ongoing need to use this project, are interested in independently developing it, or would like to maintain patches for the open source software community, please create your own fork of this project.

# SigOpt-Lite

SigOpt-Lite is an open source tool for locally running a lightweight version of [SigOpt](https://www.sigopt.com). Sigopt-Lite contains all the computation elements of SigOpt-Server, but bypasses the need to set up servers and Docker. SigOpt-Lite gives access to the SigOpt Core Module functionality; to learn more about that, visit the [SigOpt documentation](https://docs.sigopt.com/intro/sigopt-api-modules). To learn about how to host your own SigOpt server, visit the [SigOpt-Server repository](https://github.com/sigopt/sigopt-server) or [the official SigOpt OSS site (sigopt.org)](https://sigopt.org/).

## Installation

Executing the following command will install both the `sigopt` client (to interact with SigOpt) and the `sigoptlite` driver (to run the computations locally).

```bash
pip install 'sigopt[lite]'
```

After that is installed, a SigOpt connection object can be created in Python with the following commands.

```python
from sigopt import Connection
conn = Connection(driver="lite")
```

From this point, SigOpt Core module functionality can be accessed through `conn`, following the same patterns as detailed in the [SigOpt documentation](https://docs.sigopt.com/core-module-api-references/quick-start).

## Basic Example

Here is an example of using SigOpt-Lite.

```python
from sigopt import Connection
conn = Connection(driver="lite")
experiment_meta = dict(
  parameters=[
    dict(name="x0", type="double", bounds=dict(min=0, max=1)),
    dict(name="x1", type="int", bounds=dict(min=0, max=10)),
  ],
  metrics=[
    dict(
      name="f",
      objective="minimize",
    )
  ],
  observation_budget=20,
)
e = conn.experiments().create(**experiment_meta)
suggestion = conn.experiments(e.id).suggestions().create()
conn.experiments(e.id).observations().create(
  suggestion=suggestion.id,
  values=[{"name": "f", "value": 2}],
)
```

More SigOpt examples can be found at the [Examples GitHub Repo](https://github.com/sigopt/sigopt-examples). Note, some of those examples will not be compatible with SigOpt-Lite, including those using the AI Module.

### Compute Mode

SigOpt-Lite supports an alternative computation mode that can generate suggestions with reduced computation burden. You can activate this compute mode at `Connection` object instantiation.

```python
from sigopt import Connection
conn = Connection(driver="lite", compute_mode="kde_only")
```

## Comparing SigOpt-Lite to Calling api.sigopt.com

To support easy use of SigOpt-Lite, the calling sequences involving the `Connection` object are meant to match those for connecting with api.sigopt.com (as is directed in the [SigOpt documentation](https://docs.sigopt.com)). Still, in pursuit of simplicitly and a minimalistic installation, SigOpt-Lite does have some limitations.

### Supported API Objects and Endpoints

Sigopt-Lite functionality matches the core module of our hosted SigOpt platform, but only certain [API endpoints](https://docs.sigopt.com/core-module-api-references/api-endpoints) are available.

| Objects          | Endpoints                                                                                                                                                                                  |
| ---------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
| Experiment       | [Create](https://docs.sigopt.com/core-module-api-references/api-endpoints/experiment-create), [Detail](https://docs.sigopt.com/core-module-api-references/api-endpoints/experiment-detail) |
| Suggestion       | [Create](https://docs.sigopt.com/core-module-api-references/api-endpoints/suggestion-create)                                                                                               |
| Observation      | [Create](https://docs.sigopt.com/core-module-api-references/api-endpoints/observation-create), [List](https://docs.sigopt.com/core-module-api-references/api-endpoints/observation-list)   |
| Best Assignments | [Detail](https://docs.sigopt.com/core-module-api-references/api-endpoints/experiment-best-assignments)                                                                                     |

## SigOpt-Lite Behavior Limitations

### Limitations

The following behavior limitations are strictly enforced in SigOpt-Lite.

- SigOpt-Lite can only run one Experiment at a time: the Experiment ID for the sole experiment is `"-1"`.
- SigOpt-Lite supports no [parallelism](https://docs.sigopt.com/advanced_experimentation/parallelism): an observation must be reported for the open suggestion before creating another unique suggestion. Parallel suggestions can be accessed through an account at [sigopt.com](https://app.sigopt.com/signup) or through the open source SigOpt-Server.
- SigOpt-Lite does not support [Grid Search](https://docs.sigopt.com/intro/main-concepts/random_search#grid-search).

### Recommended Limitations

The following set of soft limitations are recommended by the SigOpt research team to ensure a pleasant experience using SigOpt-Lite.

- Maximum number of parameters <= 100
- Maximum number of observations <= 10,000
- Maximum categorical breadth (total number of categorical values across all [categorical parameters](https://docs.sigopt.com/intro/main-concepts/parameter_space#categorical-parameters) <= 10
- Maximum number of [parameter constraints](https://docs.sigopt.com/advanced_experimentation/parameter_constraints) <= 10
- Maximum number of unique integer constrained variables <= 10
- Maximum number of [metric constraints](https://docs.sigopt.com/advanced_experimentation/metric_constraints) <= 4
- Maximum conditionals breadth (total number of conditional values across all [conditional parameters](https://docs.sigopt.com/intro/main-concepts/parameter_space#define-conditional-parameters) <= 6

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