pipefitter.pipeline.Pipeline.transform¶
-
Pipeline.
transform
(table, *args, **kwargs)¶ Execute the transformations in this pipeline only
Parameters: table : data set
Any data set object supported by the transformers and estimators in the pipeline stages
*args : positional parameters, optional
Any valid parameters to the transformers’
transform
method**kwargs : keyword parameters, optional
Any valid keyword parameters to the transformers’
transform
methodReturns: data set
The same type of data set as passed in table
Notes
When the pipeline contains estimators, they typically just pass the input table on to the next stage of the pipeline.
Examples
Basic pipeline fit using imputers and an estimator:
>>> mean_imp = Imputer(Imputer.MEAN) >>> mode_imp = Imputer(Imputer.MODE) >>> dtree = DecisionTree(target='Origin', ... nominals=['Type', 'Cylinders', 'Origin'], ... inputs=['MPG_City', 'MPG_Highway', 'Length', ... 'Weight', 'Type', 'Cylinders']) >>> pipe = Pipeline([mean_imp, mode_imp, dtree]) >>> new_table = pipe.transform(data)