sktools 0.1.4

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.

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.