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Scikit-Learn practical pre-defined Pipelines Hub.

This package is still at an experimental stage.

Install scikit-pipes

We advise to install scikit-pipes using a virtual env, inside the env use:

pip install scikit-pipes

import pandas as pd
import numpy as np
from skpipes.pipeline import SkPipeline

data = [{"x1": 1, "x2": 400, "x3": np.nan},
        {"x1": 4.8, "x2": 250, "x3": 50},
        {"x1": 3, "x2": 140, "x3": 43},
        {"x1": 1.4, "x2": 357, "x3": 75},
        {"x1": 2.4, "x2": np.nan, "x3": 42},
        {"x1": 4, "x2": 287, "x3": 21}]

df = pd.DataFrame(data)

pipe = SkPipeline(name='median_imputer-minmax',
                  data_type="numerical")
pipe.steps
str(pipe)

pipe.fit(df)
pipe.transform(df)
pipe.fit_transform(df)

See the changelog for notes on the changes of Sklearn-pipes

• Official source code repo: https://github.com/rodrigo-arenas/scikit-pipes/
• Download releases: https://pypi.org/project/scikit-pipes/
• Issue tracker: https://github.com/rodrigo-arenas/scikit-pipes/issues
• Stable documentation: https://scikit-pipes.readthedocs.io/en/stable/

You can check the latest development version with the command:

git clone https://github.com/rodrigo-arenas/scikit-pipes.git

Install the development dependencies:

pip install -r dev-requirements.txt

Check the latest in-development documentation: https://scikit-pipes.readthedocs.io/en/latest/

Contributions are always welcome! If you want to contribute, make sure to read the Contribution guide.

Thanks to the people who are helping with this project!

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After installation, you can launch the test suite from outside the source directory:

pytest skpipes