https://github.com/scikit-learn-contrib/skdag

A more flexible alternative to scikit-learn Pipelines
https://github.com/scikit-learn-contrib/skdag

Last synced: 3 months ago
JSON representation

A more flexible alternative to scikit-learn Pipelines

• Host: GitHub
• URL: https://github.com/scikit-learn-contrib/skdag
• Owner: scikit-learn-contrib
• License: mit
• Created: 2022-06-22T20:12:13.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-06-15T20:11:14.000Z (about 1 year ago)
• Last Synced: 2025-03-24T19:05:51.851Z (3 months ago)
• Language: Python
• Size: 4.31 MB
• Stars: 32
• Watchers: 5
• Forks: 8
• Open Issues: 4
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

Awesome Lists containing this project

README

        
.. -*- mode: rst -*-

|AppVeyor|_ |Codecov|_ |ReadTheDocs|_

.. |AppVeyor| image:: https://ci.appveyor.com/api/projects/status/github/big-o/skdag?branch=main&svg=true

.. _AppVeyor: https://ci.appveyor.com/project/big-o/skdag

.. |Codecov| image:: https://codecov.io/gh/big-o/skdag/branch/main/graph/badge.svg

.. _Codecov: https://codecov.io/gh/big-o/skdag

.. |ReadTheDocs| image:: https://readthedocs.org/projects/skdag/badge/?version=latest

.. _ReadTheDocs: https://skdag.readthedocs.io/en/latest/?badge=latest

skdag - A more flexible alternative to scikit-learn Pipelines

=============================================================

.. image:: img/skdag-banner.png

scikit-dag (``skdag``) is an open-sourced, MIT-licenced library that provides advanced

workflow management to any machine learning operations that follow

scikit-learn_ conventions. Installation is simple:

.. code-block:: bash

    pip install skdag

It works by introducing Directed Acyclic Graphs as a drop-in replacement for traditional

scikit-learn ``Pipeline``. This gives you a simple interface for a range of use cases

including complex pre-processing, model stacking and benchmarking.

.. code-block:: python

   from skdag import DAGBuilder

   dag = (

      DAGBuilder(infer_dataframe=True)

      .add_step("impute", SimpleImputer())

      .add_step("vitals", "passthrough", deps={"impute": ["age", "sex", "bmi", "bp"]})

      .add_step(

         "blood",

         PCA(n_components=2, random_state=0),

         deps={"impute": ["s1", "s2", "s3", "s4", "s5", "s6"]}

      )

      .add_step(

         "rf",

         RandomForestRegressor(max_depth=5, random_state=0),

         deps=["blood", "vitals"]

      )

      .add_step("svm", SVR(C=0.7), deps=["blood", "vitals"])

      .add_step(

         "knn",

         KNeighborsRegressor(n_neighbors=5),

         deps=["blood", "vitals"]

      )

      .add_step("meta", LinearRegression(), deps=["rf", "svm", "knn"])

      .make_dag()

   )

   dag.show(detailed=True)

.. image:: doc/_static/img/cover.png

The above DAG imputes missing values, runs PCA on the columns relating to blood test

results and leaves the other columns as they are. Then they get passed to three

different regressors before being passed onto a final meta-estimator. Because DAGs

(unlike pipelines) allow predictors in the middle or a workflow, you can use them to

implement model stacking. We also chose to run the DAG steps in parallel wherever

possible.

After building our DAG, we can treat it as any other estimator:

.. code-block:: python

   from sklearn import datasets

   X, y = datasets.load_diabetes(return_X_y=True, as_frame=True)

   X_train, X_test, y_train, y_test = train_test_split(

      X, y, test_size=0.2, random_state=0

   )

   dag.fit(X_train, y_train)

   dag.predict(X_test)

Just like a pipeline, you can optimise it with a gridsearch, pickle it etc.

Note that this package does not deal with things like delayed dependencies and

distributed architectures - consider an `established `_

`solution `_ for such use cases. ``skdag`` is just for building and

executing local ensembles from estimators.

`Read on `_ to learn more about ``skdag``...

.. _scikit-learn: https://scikit-learn.org