Metadata-Version: 2.1
Name: sklearn-utilities
Version: 0.2.3
Summary: Utilities for scikit-learn.
Home-page: https://github.com/34j/sklearn-utilities
License: MIT
Author: 34j
Author-email: 34j.95a2p@simplelogin.com
Requires-Python: >=3.9,<3.13
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Software Development :: Libraries
Requires-Dist: lightgbm (>=4.1.0,<5.0.0)
Requires-Dist: pandas (>=2.1.1,<3.0.0)
Requires-Dist: scikit-learn (>=1.3.1,<2.0.0)
Requires-Dist: tqdm-joblib (>=0.0.3,<0.0.4)
Requires-Dist: typing-extensions (>=4.8.0,<5.0.0)
Project-URL: Bug Tracker, https://github.com/34j/sklearn-utilities/issues
Project-URL: Changelog, https://github.com/34j/sklearn-utilities/blob/main/CHANGELOG.md
Project-URL: Documentation, https://sklearn-utilities.readthedocs.io
Project-URL: Repository, https://github.com/34j/sklearn-utilities
Description-Content-Type: text/markdown

# Sklearn Utilities

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  <a href="https://python-poetry.org/">
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</p>

Utilities for scikit-learn.

## Installation

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

```shell
pip install sklearn-utilities
```

## API

- `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`.
- `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`: compose mean and std/var estimators.
- `DummyRegressorVar`: `DummyRegressor` that returns 1.0 for std/var.
- `TransformedTargetRegressorVar`: `TransformedTargetRegressor` with std/var support.

### `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`: reshape X and y for `nn.Linear` and `nn.Conv1d/2d` respectively. (For `nn.Conv2d`, uses `np.sliding_window_view()`.)

## See also

- [ml-tooling/best-of-ml-python](https://github.com/ml-tooling/best-of-ml-python)

## Contributors ✨

Thanks goes to these wonderful people ([emoji key](https://allcontributors.org/docs/en/emoji-key)):

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This project follows the [all-contributors](https://github.com/all-contributors/all-contributors) specification. Contributions of any kind welcome!

