Algorithmic inspection for trustworthy ML models
Install
Install the latest version of FixOut from PyPI using
pip install fixout
Getting started
How to start analysing a simple model (let's say you have trained a binary classifier on the German Credit Data):
from fixout.artifact import FixOutArtifact
from fixout.runner import FixOutRunner
fxo = FixOutRunner("Credit Risk Assessment (German Credit)")
# Indicate the sensitive features
sensitive_features = ["foreignworker","statussex"]
# Create a FixOut Artifact with your model and data
fxa = FixOutArtifact(model=model,
training_data=(X_train,y_train),
testing_data=[(X_test,y_test,"Testing")],
features_name=features_name,
sensitive_features=sensitive_features,
dictionary=dic)
Using a Jupyter Notebook
Then run the inspection with the method runJ
fxo.runJ(fxa, show=False)
You can now check the calculated fairness metrics by using the method fairness.
fxo.fairness()
In your quality management code
If you prefer to integrate FixOut into your code, then run the inspection by calling run
fxo.run(fxa, show=True)
In this case, you can access the generated dashboard at http://localhost:5000 ;)
You should be able to see an interface similar to the following
2 maintainers
Release files for fixout 0.1.35
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
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| fixout-0.1.35.tar.gz | 4.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| fixout-0.1.35-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 9.4 MB
Release files / fixout-0.1.35.tar.gz
| Download URL | fixout-0.1.35.tar.gz |
|---|---|
| Size | 4.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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Transparency logRelease files / fixout-0.1.35-py3-none-any.whl
| Download URL | fixout-0.1.35-py3-none-any.whl |
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| Size | 4.8 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.12.9
|
Provenance
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