Comments (4)
Hi @tonydp03,
Nice to meet you. From looking at the raw CSV of input data, it seems that there is a leading space before every value. So in this case, the value you're conditioning on should be " Female"
(with the leading space) instead of Female
(with no space).
BTW if your project allows for it, I would recommend accessing the CTGAN model through the SDV library. The SDV is a publicly available Python SDK that allows you to generate synthetic data using a variety of synthesizers such as CTGAN. It also providers convenient wrappers for data pre- and post-processing, should you want to modify that. And you can use conditional sampling with it too.
Some resources:
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Hi @tonydp03,
Nice to meet you. From looking at the raw CSV of input data, it seems that there is a leading space before every value. So in this case, the value you're conditioning on should be
" Female"
(with the leading space) instead ofFemale
(with no space).BTW if your project allows for it, I would recommend accessing the CTGAN model through the SDV library. The SDV is a publicly available Python SDK that allows you to generate synthetic data using a variety of synthesizers such as CTGAN. It also providers convenient wrappers for data pre- and post-processing, should you want to modify that. And you can use conditional sampling with it too.
Some resources:
Hi @npatki,
thanks for your answer. I simply assumed the test dataset could be used "out-of-the-box" and didn't notice the leading space at the beginning of the column value. I will give it another try, for sure.
Thanks for the resources too. For the moment, we were just testing the usage of CTGAN to generate synthetic data, as we were positively impressed by the results shown in the paper. In parallel, we're also testing the usage of the SDV library, as it seems an interesting tool.
from ctgan.
One more thing: is it correct that, in the main.py, the function fit
is called even when the model is loaded? I was expecting for it to be called only when the model has not been trained yet and I'm creating a new one.
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Hi @tonydp03 my apologies for getting this reply so late.
The current recommended approach is to use CTGAN via the SDV library as described above. I can answer your usage questions and help you troubleshoot any issues with your project.
Unfortunately I'm unable to go through any detailed lines of code with you. Please also note that some code in the repo may be deprecated or unsupported so I would always recommend the docs for the latest supported usage.
Thanks and please feel free to file a new issue with additional questions or feature requests.
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Related Issues (20)
- TypeError while ctgan.fit() HOT 6
- Improve DataSampler efficiency
- ValueError: mismatch of shapes when sampling data for compas dataset HOT 2
- Return loss values as float values not PyTorch objects
- Transition from using setup.py to pyproject.toml to specify project metadata
- Remove bumpversion and use bump-my-version
- Switch to using ruff for Python linting and code formatting
- Add dependency checker
- Remove scikit-learn dependency
- CTGAN using deprecated 'sklearn' HOT 2
- Replace integration test that uses the iris demo data
- Add bandit workflow
- Feature Request: More verbose logging HOT 3
- Fix minimum version workflow when pointing to github branch
- Deployment requirements based on libtorch or ONNX HOT 3
- How to load this model directly to generate data after saving it HOT 4
- Cleanup automated PR workflows
- Remove FutureWarning: Setting an item of incompatible dtype is deprecated
- Only run unit and integration tests on oldest and latest python versions for macos
- [HELP] CTGAN has Reproducibility? HOT 7
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