AutoRA Falsification Experimentalist
Project description
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/
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.pooler.falsification import falsification_pool"
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Close
Hashes for autora-experimentalist-falsification-1.0.4.tar.gz
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6900a275515299a56f64fe58f4903df4fe12ed09ce5c31809ccdc70ddb6c1be7 |
|
MD5 | 71f977ab5330ae8d5d37ccc758e570cd |
|
BLAKE2b-256 | b17464165bebdcac43471f854f77e278fed2d3bee88fe0faaf9a481c21452b63 |
Close
Hashes for autora_experimentalist_falsification-1.0.4-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b488641d8678efd5a4750d2b9d52c678c4ef1656ef1ecf57157dc77c41d5f4b2 |
|
MD5 | 7d22ff8f938d731290daf88b40505da7 |
|
BLAKE2b-256 | 3ebe3293755f8f816769314ff576c3a11c63b5442d097831b823756f5c14b9fd |