make_pipeline
- imbens.pipeline.make_pipeline(*steps, memory=None, verbose=False)
Construct a Pipeline from the given estimators.
This is a shorthand for the Pipeline constructor; it does not require, and does not permit, naming the estimators. Instead, their names will be set to the lowercase of their types automatically.
- Parameters:
- *stepslist of estimators
A list of estimators.
- memoryNone, str or object with the joblib.Memory interface, default=None
Used to cache the fitted transformers of the pipeline. By default, no caching is performed. If a string is given, it is the path to the caching directory. Enabling caching triggers a clone of the transformers before fitting. Therefore, the transformer instance given to the pipeline cannot be inspected directly. Use the attribute
named_stepsorstepsto inspect estimators within the pipeline. Caching the transformers is advantageous when fitting is time consuming.- verbosebool, default=False
If True, the time elapsed while fitting each step will be printed as it is completed.
- Returns:
- pPipeline
See also
imbens.pipeline.PipelineClass for creating a pipeline of transforms with a final estimator.
Examples
>>> from sklearn.naive_bayes import GaussianNB >>> from sklearn.preprocessing import StandardScaler >>> make_pipeline(StandardScaler(), GaussianNB(priors=None)) ... Pipeline(steps=[('standardscaler', StandardScaler()), ('gaussiannb', GaussianNB())])
