scikit-dict

Define Scikit-Learn objects from dict

Getting Started

Installation

Use the package manager pip to install scikit-dict.

pip install scikit-dict

Usage

Create a dict from a Scikit-Learn object.

import skdict
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC

obj = Pipeline([("scaler", StandardScaler()), ("svc", sklearn.svm.SVC())])
d = skdict.dump(obj)

It will create a dict with this content:

{
    "Pipeline": {
        "steps": [
            [
                "scaler",
                {"StandardScaler": None}
            ],
            [
                "svc",
                {"SVC": None}
            ]
        ]
        }
}

Define a Scikit-Learn object from a dict.

Recreate the original pipeline.

import skdict

skdict.load(d)

Why should I use this?

This package aims to make it easier to export pipelines to YAML or JSON files.

The goal is to decouple the pipeline from the executing code, so the user can focus only on the pipeline itself.

It also make it easier to quickly switching in between pipelines, and log it as artifacts on experiment tracking tools (e.g. MLFlow). It works better alongside CLI applications.

Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

License

This project is licensed under the MIT License.

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