obsidian-apo 0.8.6

Automated experiment design and black-box optimization

obsidian

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

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.
4. 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.
5. 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

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

campaign.add_data(Z0)
campaign.fit()

Suggest New Experiments

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

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

optim_progress(campaign)

from obsidian.campaign import Explainer
exp = Explainer(campaiagn)
exp.shap_explain()
exp.shap_summary()

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

Installation

The latest obsidian release can be installed using pip:

pip install obsidian-apo

To install the required dependencies for running the Dash app:

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

Contributing

See CONTRIBUTING to learn more.

Developers

• Kevin Stone (Merck & Co., Inc.) [e-mail]
• Yuting Xu (Merck & Co., Inc.) [e-mail]

Contributors

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

License

obsidian is licensed by the GPLv3 license.