Skip to main content

Utilities for scikit-learn.

Project description

Sklearn Utilities

CI Status Documentation Status Test coverage percentage

Poetry black pre-commit

PyPI Version Supported Python versions License

Utilities for scikit-learn.

Installation

Install this via pip (or your favourite package manager):

pip install sklearn-utilities

API

See Docs for more information.

  • EstimatorWrapperBase: base class for wrappers. Redirects all attributes which are not in the wrapper to the wrapped estimator.
  • DataFrameWrapper: tries to convert every estimator output to a pandas DataFrame or Series.
  • FeatureUnionPandas: a FeatureUnion that works with pandas DataFrames.
  • IncludedColumnTransformerPandas, ExcludedColumnTransformerPandas: select columns by name.
  • AppendPredictionToX: appends the prediction of y to X.
  • AppendXPredictionToX: appends the prediction of X to X.
  • DropByNoisePrediction: drops columns which has high importance in predicting noise.
  • DropMissingColumns: drops columns with missing values above a threshold.
  • DropMissingRowsY: drops rows with missing values in y. Use feature_engine.DropMissingData for X.
  • IntersectXY: drops rows where the index of X and y do not intersect. Use with feature_engine.DropMissingData.
  • ReindexMissingColumns: reindexes columns of X in transform() to match the columns of X in fit().
  • ReportNonFinite: reports non-finite values in X and/or y.
  • IdTransformer: a transformer that does nothing.
  • RecursiveFitSubtractRegressor: a regressor that recursively fits a regressor and subtracts the prediction from the target.
  • SmartMultioutputEstimator: a MultiOutputEstimator that supports tuple of arrays in predict() and supports pandas Series and DataFrame.
  • until_event(), since_event(): calculates the time since or until events (Series[bool])
  • ComposeVarEstimator: composes mean and std/var estimators.
  • DummyRegressorVar: DummyRegressor that returns 1.0 for std/var.
  • TransformedTargetRegressorVar: TransformedTargetRegressor with std/var support.
  • StandardScalerVar: StandardScaler with std/var support.
  • EvalSetWrapper, CatBoostProgressBarWrapper: wrapper that passes eval_set to fit() using train_test_split(), mainly for CatBoost. The latter shows progress bar (using tqdm) as well. Useful for early stopping. For LightGBM, see lightgbm-callbacks.

sklearn_utilities.dataset

  • add_missing_values(): adds missing values to a dataset.

sklearn_utilities.torch

  • PCATorch: faster PCA using PyTorch with GPU support.

sklearn_utilities.torch.skorch

  • SkorchReshaper, SkorchCNNReshaper: reshapes X and y for nn.Linear and nn.Conv1d/2d respectively. (For nn.Conv2d, uses np.sliding_window_view().)
  • AllowNaN: wraps a loss module and assign 0 to y and y_hat for indices where y contains NaN in forward()..

See also

Contributors ✨

Thanks goes to these wonderful people (emoji key):

This project follows the all-contributors specification. Contributions of any kind welcome!

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sklearn_utilities-0.5.11.tar.gz (30.3 kB view details)

Uploaded Source

Built Distribution

sklearn_utilities-0.5.11-py3-none-any.whl (40.2 kB view details)

Uploaded Python 3

File details

Details for the file sklearn_utilities-0.5.11.tar.gz.

File metadata

  • Download URL: sklearn_utilities-0.5.11.tar.gz
  • Upload date:
  • Size: 30.3 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.1 CPython/3.12.7

File hashes

Hashes for sklearn_utilities-0.5.11.tar.gz
Algorithm Hash digest
SHA256 34eded6c25640f1aa6069ad1e013659d3af133f497f361b9413007423d409501
MD5 9face0a1ecd92af60b7561fa8bbbb2e3
BLAKE2b-256 159742b529437206276eca5cdd5b9827dd359c64bb851e08ac5f41210296ef54

See more details on using hashes here.

File details

Details for the file sklearn_utilities-0.5.11-py3-none-any.whl.

File metadata

File hashes

Hashes for sklearn_utilities-0.5.11-py3-none-any.whl
Algorithm Hash digest
SHA256 c5fdd4aa8e857aa88b15c4ba33fea4aef54a83db67dd22b8c29fc7c7b7d3bcc9
MD5 9f766b2f90c6471291c3338c6dd876f4
BLAKE2b-256 4a06c4b6a5d3eac1ffc370ad3038834714fbe2f48c73da1b1cc6f8362f8c88b2

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page