Tools to extend sklearn
Project description
sktools
sktools provides tools to extend sklearn, like several feature engineering based transformers.
Installation
To install sktools, run this command in your terminal:
$ pip install sktools
Documentation
Can be found in https://sktools.readthedocs.io
Usage
from sktools import IsEmptyExtractor from sklearn.linear_model import LogisticRegression from sklearn.pipeline import Pipeline ... mod = Pipeline([ ("impute-features", IsEmptyExtractor()), ("model", LogisticRegression()) ]) ...
Features
Here’s a list of features that sktools currently offers:
- sktools.encoders.NestedTargetEncoder performs target encoding suited for variables with nesting.
- sktools.encoders.QuantileEncoder performs target aggregation using a quantile instead of the mean.
- sktools.preprocessing.CyclicFeaturizer converts numeric to cyclical features via sine and cosine transformations.
- sktools.impute.IsEmptyExtractor creates binary variables indicating if there are missing values.
- sktools.matrix_denser.MatrixDenser transformer that converts sparse matrices to dense.
- sktools.quantilegroups.GroupedQuantileTransformer creates quantiles of a feature by group.
- sktools.quantilegroups.PercentileGroupFeaturizer creates features regarding how an instance compares with a quantile of its group.
- sktools.quantilegroups.MeanGroupFeaturizer creates features regarding how an instance compares with the mean of its group.
- sktools.selectors.TypeSelector gets variables matching a type.
- sktools.selectors.ItemsSelector allows to manually choose some variables.
- sktools.ensemble.MedianForestRegressor applies the median instead of the mean when aggregating trees predictions.
- sktools.linear_model.QuantileRegression sklearn style wrapper for quantile regression.
- sktools.model_selection.BootstrapFold bootstrap cross-validator.
- sktools.GradientBoostingFeatureGenerator Automated feature generation through gradient boosting.
License
MIT license
Credits
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
History
0.1.4 (2021-03-20)
- Gradient boosting feature regressor
0.1.3 (2020-07-13)
- Bootstrap cross-validation
- Cyclic featurizer
0.1.2 (2020-06-24)
- L1 linear model and random forest
- Quantile encoder refactor
0.1.1 (2020-06-10)
- Refactor code, add group featurizers
0.1.0 (2020-04-19)
- First release on PyPI.
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