snaplib

A simple data preprocessing tools.

user guide

Kaggle Notebook

Classification

https://www.kaggle.com/code/artyomkolas/titanic-snaplib-classification/notebook

Regression

https://www.kaggle.com/code/artyomkolas/housing-prices-with-snaplib/notebook

PyPi

!pip install snaplib
from snaplib.snaplib import Snaplib
sl = Snaplib()
  1. sl.nan_info
  2. sl.nan_plot
  3. sl.cleane
  4. sl.recover_data - NaN imputing with ML
  5. sl.dummied
  6. sl.encode_dataframe
  7. sl.decode_dataframe
  8. sl.train_test_split_balanced
  9. sl.k_folds_split

For one and list of algorithms with bagging

  1. sl.cross_val
  2. sl.features_selection_regr
  3. sl.features_selection_clsf
  4. sl.fit_stacked
  5. sl.save_stack
  6. sl.load_stack
  7. sl.predict_stacked

doc

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

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ArtyomKolas from gravatar.com ArtyomKolas

Credits

Author: Artsiom Kolas

License

GNU General Public License v3 (GPLv3)

Classifiers

Report project as malware

Metadata

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ArtyomKolas from gravatar.com ArtyomKolas

Credits

Author: Artsiom Kolas

License

GNU General Public License v3 (GPLv3)

Classifiers

Report project as malware

Release files for snaplib 0.4.96

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