Scikit-Learn useful pre-defined Pipelines Hub

Scikit-Pipes

Scikit-Learn useful pre-defined Pipelines Hub

Usage:

Install scikit-pipes

It's advised to install sklearn-genetic 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='imputer_median-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-genetic-opt

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/

Testing

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

pytest skpipes