Scikit-Pipes

Scikit-Learn practical pre-defined Pipelines Hub.

This package is still at an experimental stage.

Usage:

Install scikit-pipes

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

pip install scikit-pipes

Example: Simple Preprocessing

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)

Changelog

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

Important links

• 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/

Source code

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/

Contributing

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!

Testing

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

pytest skpipes