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.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
File details
Details for the file autora_experimentalist_falsification-2.2.0.tar.gz
.
File metadata
- Download URL: autora_experimentalist_falsification-2.2.0.tar.gz
- Upload date:
- Size: 382.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.20
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4a58599427d15dec7fa7e3e47c44ac86c62bd2131800865aa93f7f658f0fcfe2 |
|
MD5 | a0530fff8e432142a20fccf9d4a9a308 |
|
BLAKE2b-256 | c5aa227d1dfe788c4295cc3fd79bc5af5d15094358afb5633451f03f8a6c0222 |
Provenance
File details
Details for the file autora_experimentalist_falsification-2.2.0-py3-none-any.whl
.
File metadata
- Download URL: autora_experimentalist_falsification-2.2.0-py3-none-any.whl
- Upload date:
- Size: 10.9 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.1.1 CPython/3.9.20
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | cba6f608b0eb1ce85a38e1a445490a4155761816b5347156b3d56c2095371a42 |
|
MD5 | b11421aa08ea9cfb28bc7063cd5d5d10 |
|
BLAKE2b-256 | 9048c29084def27160cd5400fd70a9c2bebe3499026994838001a64299a803cc |