Metadata-Version: 2.1
Name: autora_experimentalist_divergence
Version: 0.0.2
Summary: sampling based on divergence
Author-email: Younes Strittmatter <younes.strittmatter@brown.edu>
License: Copyright (c) 2024 Younes Strittmatter
        
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Project-URL: homepage, http://www.empiricalresearch.ai
Project-URL: repository, https://github.com/AutoResearch/autora-experimentalist-divergence
Project-URL: documentation, https://autoresearch.github.io/autora/
Requires-Python: <4,>=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: autora-core
Requires-Dist: scikit-learn
Provides-Extra: dev
Requires-Dist: autora-core[dev]; extra == "dev"

# AutoRA Divergence Experimentalist

The divergence experimentalist identifies experimental conditions $\vec{x}' \in X'$ with respect the
distance between existing experimental data $\vec{x}, $\vec{y} and data predicted by a model 
$\vec{x_pool}, $\vec{y_pred}:

$$
\underset{\vec{x}'}{\arg\max}~sum(d((\vec{x}, \vec{y}), (\vec{x_pool}, \vec{y_pred}))
$$

The aim of this experimentalist is to combine novelty and uncertainty by using a distance that
combines both: The distance between existing conditions to new conditions and the distance of 
existing observations to predictions.
