https://www.kaggle.com/code/artyomkolas/titanic-snaplib-classification/notebook
https://www.kaggle.com/code/artyomkolas/housing-prices-with-snaplib/notebook
!pip install snaplib
from snaplib.snaplib import Snaplib
sl = Snaplib()
print(sl.recover_data.__doc__)
Imputing of missing values (np.nan) in tabular data, not TimeSeries.
Use case:
df = Snaplib().recover_data(df, device="cpu", verbose=True)
device must be "cpu" or "gpu". Sometime small datasets work faster with cpu.
verbose = True algorithm runs cross validation tests and prints results of tests for decision making.
discrete_columns = ['col_name_1', 'col_name_2', 'col_name_3', 'etc']
TESTS on https://www.kaggle.com/code/artyomkolas/nan-prediction-in-progress/notebook
FAQs
Data preprocessing lib
We found that snaplib demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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