Define Scikit-Learn objects from dict
Use the package manager pip to install scikit-dict.
pip install scikit-dict
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}
]
]
}
}
Recreate the original pipeline.
import skdict
skdict.load(d)
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.
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.
This project is licensed under the MIT License.