Comments (5)
Hmm, that interesting. The error reproduced on a collab with python 3.7.12 but work smoothly on my pc with 3.8.2, 3.8.5 and on Kaggle machine with python 3.7.10
https://www.kaggle.com/insaff/tabular-gan-demo/log
from gan-for-tabular-data.
I will continue the investigation. As temporary workaround use Kaggle notebooks or different python
from gan-for-tabular-data.
Dear Insaf Ashrapov, thank you very much for your kind help and recommendations. We will try it alternatively. However, if you will find the answer, we would very glad to hear about it.
I wish you a nice week.
Lubomir
from gan-for-tabular-data.
Finally, I came to the solution. Colab already has installed sklearn so I have to manually install the right version:
!pip install scikit_learn==0.23.2ยง
from gan-for-tabular-data.
@la269342 Fixed in the new version. Install this way:
!pip install tabgan==1.1.2
In colab you have to reload runtime
from gan-for-tabular-data.
Related Issues (20)
- is it ok for regression type task? HOT 1
- generated Cov is not that close HOT 2
- all sample codes not working till epoch end HOT 1
- second args in generate_data_pipe cannot be left None HOT 2
- TypeError: unsupported operand type(s) for +: 'NoneType' and 'NoneType' HOT 1
- training CTGAN stops in the middle (around 24%) HOT 2
- Difference between OriginalGenerator and GANGenerator HOT 1
- Getting this error when trying to install load HOT 2
- check HOT 1
- ContextualVersionConflict: (scikit-learn 1.0.2 (/usr/local/lib/python3.7/dist-packages), Requirement.parse('scikit-learn==0.23.2'), {'tabgan'}) HOT 3
- Dear Author, May I know the ctgan version for the installation? I am getting error. from ctgan import _CTGANSynthesizer ImportError: cannot import name '_CTGANSynthesizer' HOT 4
- pip install scikit-learn version issue HOT 3
- Mistake in Readme HOT 2
- Some issues araised when running Tab-GAN: 1) Manage Categorical Variables. 2) Batch size problem HOT 8
- Reproducibility issue HOT 1
- ValueError: Input X contains NaN although NaN filtered HOT 7
- IntCastingNaNError Despite No NaN values HOT 3
- LGBMClassifier.fit() got an unexpected keyword argument 'early_stopping_rounds' HOT 2
- Dependency issue with ForestDiffusion Generator HOT 3
- TypeError w/ Boolean Data HOT 1
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