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