obsidian-apo 0.8.6
Automated experiment design and black-box optimization
Requires Python: <4.0,>=3.10
Keywords: optimization, experiment design, Bayesian optimization, process development, APO, DOE

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<div align="center">

<h1> obsidian</h1>

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__obsidian__ is a library for algorithmic process design and black-box optimization using AI-guided experiment design

</div>

```code
pip install obsidian-apo
```

The _obsidian_ library offers a set of modules for designing, executing, analyzing, and visualizing algorithmic process optimization (APO) using sample-efficient strategies such as Bayesian Optimization (BO). _obsidian_ uses highly flexible models to build internal representations of the measured system in a way that can be explored for characterization and exploited for maximization based on uncertainty estimation and exploration strategies. _obsidian_ supports batch experimentation (joint optimization and parallel evaluation) and is highly configurable for varying use cases, although the default specifications are encouraged.

_We thank you for your patience and invite you to collaborate with us while __obsidian__ is in beta!_

 # Key Features

 1. __End-User-Friendly__: Designed to elevate the average process development scientist. No machine learning experience required.
 2. __Deployable__ using pre-built _Dash_ application (`pip install obsidian-apo[app]`). Each class is fully serializable, without third-party packages, to enable web-based API usage. 
 3. __Explainable__ and visualizable using SHAP analysis and interactive figures.
 5. __Flexible__: Handles any input (numeric, discrete) and optionally input/output constraints, multiple outcomes, batch optimization, and a variety of novelty objective compositions. We know that experiment campaigns can have fluctuating objectives and resources, and _obsidian_ is built to support that.
 6. __Purpose-Driven Development__: Impactful features proposed, developed, maintained, and used by laboratory bench scientists. Revelantly designed for process development, optimization, and characterization.

# How it Works: Algorithmic Optimization
The workflow for algorithmic process optimization is an iterative workflow of the following steps:

1. Collect data
2. Fit a model to the data and estimate uncertainty across a design space
3. Search for new experiments and evaluate for objective and/or informational utility
4. Design experiments where utility is maximized
5. Repeat

The central object ob the __obsidian__ library is the `BayesianOptimizer`, which can be optionally wrapped by a `Campaign`. A bayesian optimization has two key components that govern the optimization:
1. The surrogate model: A black-box model which is regressed to data and used for inference. Most often a _Gaussian Process_ (`surrogate='GP'`).
2. The acquisition function: A mathematical description of the quality of potential experiments, as it pertains to optimization. Most often _Expected Improvement_ (`acquisition=['EI']`).

# Usage Example

## Specify Parameters & Initialize a Design

```python
from obsidian import Campaign, ParamSpace, Target
from obsidian.parameters import Param_Categorical, Param_Ordinal, Param_Continuous

params = [
    Param_Continuous('Temperature', -10, 30),
    Param_Continuous('Concentration', 10, 150),
    Param_Continuous('Enzyme', 0.01, 0.30),
    Param_Categorical('Variant', ['MRK001', 'MRK002', 'MRK003']),
    Param_Ordinal('Stir Rate', ['Low', 'Medium', 'High']),
    ]

X_space = ParamSpace(params)
target = Target('Yield', aim='max')
campaign = Campaign(X_space, target)
X0 = campaign.designer.initialize(10, 'LHS', seed=0)
```

|    |   Temperature |   Concentration |   Enzyme | Variant   | Stir Rate   |
|---:|--------------:|----------------:|---------:|:----------|:------------|
|  0 |             8 |              17 |   0.1405 | MRK001    | Medium      |
|  1 |            12 |             143 |   0.1695 | MRK003    | Medium      |
|  2 |             4 |             101 |   0.2855 | MRK002    | High        |
|  3 |            28 |              87 |   0.1115 | MRK002    | Low         |
|  4 |            -4 |             115 |   0.2275 | MRK001    | Low         |
|  5 |            -8 |              73 |   0.0825 | MRK002    | Medium      |
|  6 |            20 |             129 |   0.0535 | MRK001    | High        |
|  7 |            24 |              31 |   0.2565 | MRK002    | Medium      |
|  8 |            16 |              59 |   0.1985 | MRK003    | High        |
|  9 |             0 |              45 |   0.0245 | MRK003    | Low         |


## Collect Data and Fit the Optimizer

```python
campaign.add_data(Z0)
campaign.fit()
```

## Suggest New Experiments

```python
campaign.suggest(m_batch=2)
```

|    |   Temperature |   Concentration |    Enzyme | Variant   | Stir Rate   |   Yield (pred) |   Yield lb |   Yield ub | aq Method   |   aq Value |
|---:|--------------:|----------------:|----------:|:----------|:------------|---------------:|-----------:|-----------:|:------------|-----------:|
|  0 |           -10 |              10 | 0.0918096 | MRK001    | Medium      |       112.497  |   102.558  |   122.436  | EI          |   0.848569 |
|  1 |           -10 |             150 | 0.0882423 | MRK002    | High        |        89.8334 |    79.8589 |    99.8079 | EI          |   0.870511 |

# Interpret Results

```python
from obsidian.plotting import surface_plot, optim_progress
surface_plot(campaign.optimizer, feature_ids=(0,2))
```

<div align='center'>
  <img src="https://github.com/MSDLLCpapers/obsidian/blob/main/docs/_static/tutorials/demo_surface.png?raw=true" width="400" alt = "obsidian app">
</div>
<br>

```python
optim_progress(campaign)
```
<div align='center'>
  <img src="https://github.com/MSDLLCpapers/obsidian/blob/main/docs/_static/tutorials/demo_optimprogress.png?raw=true" width="400" alt = "obsidian app">
</div>
<br>

```python
from obsidian.campaign import Explainer
exp = Explainer(campaiagn)
exp.shap_explain()
exp.shap_summary()
```
<div align='center'>
  <img src="https://github.com/MSDLLCpapers/obsidian/blob/main/docs/_static/tutorials/demo_shap.png?raw=true" width="400" alt = "obsidian app">
</div>
<br>

```python
exp.shap_pdp_ice(ind = 2, ice_color_var = 3)
```

<div align='center'>
  <img src="https://github.com/MSDLLCpapers/obsidian/blob/main/docs/_static/tutorials/demo_pdp_ice.png?raw=true" width="400" alt = "obsidian app">
</div>
<br>

# Installation

The latest _obsidian_ release can be installed using pip:

```python
pip install obsidian-apo
```

To install the required dependencies for running the _Dash_ app:
```python
pip install obsidian-apo[app]
```

Be sure to `pip` install in a newly created `conda` environment to avoid dependency conflicts.

# Launching the App

Clone this repository using the following command:
```
git clone https://github.com/MSDLLCpapers/obsidian.git
```

With `obsidian-apo[app]` installed on an activate Python environment, navigate to the new folder and simply run the folloiwng command:
```
python app.py
```
<div align='center'>
  <img src="https://github.com/MSDLLCpapers/obsidian/blob/main/docs/_static/tutorials/demo_app.png?raw=true" width="600" alt = "obsidian app">
</div>
<br>

# Contributing

See [CONTRIBUTING](https://github.com/MSDLLCpapers/obsidian/blob/main/CONTRIBUTING.md) to learn more.

## Developers

- Kevin Stone (Merck & Co., Inc.) [kevin.stone@merck.com](mailto:kevin.stone@merck.com)
- Yuting Xu (Merck & Co., Inc.) [yuting.xu@merck.com](mailto:yuting.xu@merck.com)

## Contributors

- Ajit Vikram (Merck & Co., Inc.)
- Melodie Christensen (Merck & Co., Inc.)
- Kobi Felton (Merck & Co., Inc.)

## License
__obsidian__ is licensed by the [GPLv3 license](https://github.com/MSDLLCpapers/obsidian/blob/main/LICENSE).