Metadata-Version: 2.1
Name: autora-experimentalist-falsification
Version: 1.0.3
Summary: AutoRA Falsification Experimentalist
Author-email: Sebastian Musslick <sebastian@musslick.de>
License: MIT License
Project-URL: homepage, http://www.empiricalresearch.ai
Project-URL: repository, https://github.com/AutoResearch/autora-experimentalist-falsification
Project-URL: documentation, https://autoresearch.github.io/autora/
Description-Content-Type: text/markdown
Provides-Extra: dev

# AutoRA Falsification Experimentalist

The falsification pooler and sampler identify novel experimental conditions $X'$ under 
which the loss $\hat{\mathcal{L}}(M,X,Y,X')$ of the best 
candidate model is predicted to be the highest. This loss is 
approximated with a multi-layer perceptron, which is trained to 
predict the loss of a candidate model, $M$, given experiment 
conditions $X$  and dependent measures $Y$ that have already been probed:

$$
\underset{X'}{argmax}~\hat{\mathcal{L}}(M,X,Y,X').
$$

## Quickstart Guide

You will need:

- `python` 3.8 or greater: [https://www.python.org/downloads/](https://www.python.org/downloads/)

*Falsification Experimentalist* is a part of the `autora` package:

```shell
pip install -U autora["experimentalist-falsification"]
```


Check your installation by running:
```shell
python -c "from autora.experimentalist.pooler.falsification import falsification_pool"
```
