sklearn-utilities 0.5.14
Utilities for scikit-learn.
License: MIT
Requires Python: <3.13,>=3.9

# Sklearn Utilities

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Utilities for scikit-learn.

## Installation

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

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

## API

See [Docs](https://sklearn-utilities.readthedocs.io/en/latest/sklearn_utilities.html) 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`](https://github.com/34j/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

- [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!