Comments (2)
Hi @gjuresic, currently it is not possible to track the loss values very easily although we have an outstanding feature request for it at #300. You can review the proposed functionality there and let us know if it will meet your needs.
In the meantime, there are still other options for seeing if the model learned distributions. One possible approach:
- Sample synthetic data from the fitted synthesizer
- Use the SDMetrics library to compare the real vs. synthetic data. If the synthesizer learned the distributions well, it should report high scores.
- You can also run a Quality Report and create visualizations to manually inspect the data.
Hopefully that helps! Let me know if you have any follow ups, but otherwise, I'd defer to issue #300 for the implementation that we want to add.
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Related Issues (20)
- Upgrade to torch 2.0
- Generate image of discriminator/generator loss values during `fit` HOT 3
- Remove upper bound for pandas
- load_demo raises urllib.error.HTTPError: HTTP Error 403: Forbidden
- Torch 2.0 fails with cuda=False
- Should a 5-Likert scale be treated as either continuous or discrete? HOT 2
- Multi GPU support
- Avoid generating the conditional column
- Add support for Python 3.11
- Add progress bar for CTGAN fitting (+ save the loss values)
- Question about large amount of training dataset in TVAE -- is there max? HOT 1
- Add verbosity TVAE (progress bar + save the loss values)
- Condition with inequality for continuous columns
- Drop support for Python 3.7
- Question regarding CTGAN for data synthesis and classification tasks
- Set generator to eval mode before sampling?
- Switch default branch from master to main
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