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:
python3.8 or greater: https://www.python.org/downloads/
Falsification Experimentalist is a part of the autora package:
pip install -U autora["experimentalist-falsification"]
Check your installation by running:
python -c "from autora.experimentalist.falsification import falsification_pool"
1 maintainer
Metadata
1 maintainer
Release files for autora-experimentalist-falsification 2.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| autora_experimentalist_falsification-2.2.0.tar.gz | 382.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| autora_experimentalist_falsification-2.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 393.4 kB
Release files / autora_experimentalist_falsification-2.2.0.tar.gz
| Download URL | autora_experimentalist_falsification-2.2.0.tar.gz |
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| Size | 382.5 kB |
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Release files / autora_experimentalist_falsification-2.2.0-py3-none-any.whl
| Download URL | autora_experimentalist_falsification-2.2.0-py3-none-any.whl |
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| Size | 10.9 kB |
| Tags | Python 3 |
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