- 1.25.0 (latest)
- 1.24.0
- 1.22.0
- 1.21.0
- 1.20.0
- 1.19.0
- 1.18.0
- 1.17.0
- 1.16.0
- 1.15.0
- 1.14.0
- 1.13.0
- 1.12.0
- 1.11.1
- 1.10.0
- 1.9.0
- 1.8.0
- 1.7.0
- 1.6.0
- 1.5.0
- 1.4.0
- 1.3.0
- 1.2.0
- 1.1.0
- 1.0.0
- 0.26.0
- 0.25.0
- 0.24.0
- 0.23.0
- 0.22.0
- 0.21.0
- 0.20.1
- 0.19.2
- 0.18.0
- 0.17.0
- 0.16.0
- 0.15.0
- 0.14.1
- 0.13.0
- 0.12.0
- 0.11.0
- 0.10.0
- 0.9.0
- 0.8.0
- 0.7.0
- 0.6.0
- 0.5.0
- 0.4.0
- 0.3.0
- 0.2.0
For composing estimators together. This module is styled after scikit-learn's pipeline module: https://scikit-learn.org/stable/modules/pipeline.html.
Classes
Pipeline
Pipeline(steps: typing.List[typing.Tuple[str, bigframes.ml.base.BaseEstimator]])
Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be transforms
. That is, they
must implement fit
and transform
methods.
The final estimator only needs to implement fit
.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters. This simplifies code and allows for
deploying an estimator and preprocessing together, e.g. with Pipeline.to_gbq(...).