vv4ml is the verification and validation library for your machine learning models and simulations. vv4ml is particular useful for predictors in medical systems.
It offers tools for performing tests that assess credibility.
In particular:
- Fundamental verification tests: that's to say
convergence,smoothnessandnumerical errortest) which handle the credibility of your model - A useful Latin hypercube sampling method: it has available a
baseandmaximinoptimization method - Different metrics:
MeanRelativeError,MeanAbsoluteError,MaximumRelativeError,MaximumAbsoluteError - YAML parser and loader: a convenient configuration file parser and loader for your model card document
Pydanticmodels: most of the project is Pydantic structured whose models are validated at runtime
Get started
Refer to the Read the Docs guide here where you will find everything you need.
For a general overlook check the index.
For a quick try look at the Jupyter notebook.
Install
See the installation page for further information.
You can quickly install vv4ml with:
pip install vv4ml
Contributing
TBA
License
TBA
3 maintainers
Metadata
3 maintainers
Release files for vv4ml 1.0.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| vv4ml-1.0.1.tar.gz | 2.4 kB | Without provenance | Details for vv4ml-1.0.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| vv4ml-1.0.1-py3-none-any.whl | Python 3 | none | any | Details for vv4ml-1.0.1-py3-none-any.whl |
Total release size: 4.5 kB
Release files / vv4ml-1.0.1.tar.gz
| Download URL | vv4ml-1.0.1.tar.gz |
|---|---|
| Size | 2.4 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / vv4ml-1.0.1-py3-none-any.whl
| Download URL | vv4ml-1.0.1-py3-none-any.whl |
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| Size | 2.1 kB |
| Tags | Python 3 |
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