Comments (1)
Hi Joe, as part of our work using SynthVAE in the synthetic data pipeline we found similar issues in terms of reproducability. We found that any metrics that use sklearn components are not reproducible and cannot be made so without changing the sdv code.
The reason for this is that setting the numpy random seed doesn't have the scope to set the sklearn random_state when it's imported from another file. As a result any metrics that use a sklearn component with a random_state argument will not be reproducible.
Metrics such as GMLogLikelihood, detection metrics e.g. logistic regression, standard vector machine all use sklearn so will be affected by this.
Although no fix is available at the moment, I wanted to add the above to give more info around the likely cause of this.
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Related Issues (14)
- Add in notebook/script to reproduce plots from report HOT 1
- Upgrade and align to Opacus v1.0.0
- Datetime handling in VAE
- Add ability to specify a different dataset
- Investigate SDV warnings around datetimes not appearing in original dataset
- Handling datetime generation
- Greater flexibility in SynthVAE model creation
- Handling Missing Data
- Expand metric options
- Poor correlations when using `GMM` preprocessing
- Update Change log for minor fixes
- VAE.py - Extract output_dim as user input
- VAE.py - Set GPU vs CPU once
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