Comments (2)
Try provided example:
import pandas as pd
import numpy as np
from tabgan.utils import get_year_mnth_dt_from_date,make_two_digit,collect_dates
from tabgan.sampler import OriginalGenerator, GANGenerator
train_size = 100
train = pd.DataFrame(
np.random.randint(-10, 150, size=(train_size, 4)), columns=list("ABCD")
)
min_date = pd.to_datetime('2019-01-01')
max_date = pd.to_datetime('2021-12-31')
d = (max_date - min_date).days + 1
train['Date'] = min_date + pd.to_timedelta(pd.np.random.randint(d, size=train_size), unit='d')
train = get_year_mnth_dt_from_date(train, 'Date')
new_train, new_target = GANGenerator(gen_x_times=1.1, cat_cols=['year'], bot_filter_quantile=0.001,
top_filter_quantile=0.999,
is_post_process=True, pregeneration_frac=2, only_generated_data=False).\
generate_data_pipe(train.drop('Date', axis=1), None,
train.drop('Date', axis=1)
)
new_train = collect_dates(new_train)
from gan-for-tabular-data.
And I believe your problem has been solved in new version
Try pip install tabgan==1.2.1
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
- 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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from gan-for-tabular-data.