tensorflow / tensorflow/fairness-indicators
Performance of CelebA constrained model
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- Dominant language
- Python
- Stars
- 358
- Forks
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Description
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Have I written custom code (as opposed to using stock example code provided): No
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OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Google Colab
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Fairness Indicators version: 0.30.1
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TensorFlow version: 2.5.0
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Python version: 3.7.10
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TFMA version: 0.30.0
Hey there,
I have noticed that the constrained model in the CelebA example Notebook has a horrible positive rate: 0.1 @ 0.5 threshold.
I expected that the model would perform at least equally good on this metric.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the CelebA constrained-model example Notebook and reproduce it in Google Colab using the reported Fairness Indicators, TensorFlow, Python, and TFMA versions. Compare the positive rate at the 0.5 threshold with the expected unconstrained behavior; the issue is done when the cause is identified and the example or its documented expectations are corrected.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100