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Wrappers

You can find an introduction to Wrappers in our Getting Started section.

An Introduction to Wrappers

Wrapper-kind pdpipe pipeline stages.

Classes

FitOnly

Bases: PdPipelineStage

A wrapper that applies a stage to input data only when fitting.

In other words, the input data is not transformed if the stage has already been fitted once.

Parameters:

Name Type Description Default
stage PdPipelineStage

The pipeline stage to operate on input data only when fitting.

 required
**kwargs object

All PdPipelineStage constructor parameters are supported.

 {}

Examples:

>>> import pandas as pd; import pdpipe as pdp;
>>> df = pd.DataFrame([[8,'a'],[5,'b']], [1,2], ['num', 'char'])
>>> stage = pdp.FitOnly(pdp.ColDrop('num'))
>>> stage(df)
  char
1    a
2    b
>>> df2 = pd.DataFrame([[8,'a'],[5,'b']], [1,2], ['num', 'char'])
>>> stage(df2)
   num char
1    8    a
2    5    b
Source code in pdpipe/wrappers.py 
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class FitOnly(PdPipelineStage):
    """
    A wrapper that applies a stage to input data only when fitting.

    In other words, the input data is not transformed if the stage has
    already been fitted once.

    Parameters
    ----------
    stage : PdPipelineStage
        The pipeline stage to operate on input data only when fitting.
    **kwargs : object
        All PdPipelineStage constructor parameters are supported.

    Examples
    --------
    >>> import pandas as pd; import pdpipe as pdp;
    >>> df = pd.DataFrame([[8,'a'],[5,'b']], [1,2], ['num', 'char'])
    >>> stage = pdp.FitOnly(pdp.ColDrop('num'))
    >>> stage(df)
      char
    1    a
    2    b
    >>> df2 = pd.DataFrame([[8,'a'],[5,'b']], [1,2], ['num', 'char'])
    >>> stage(df2)
       num char
    1    8    a
    2    5    b
    """

    _FITONLY_DESC = "Applying, only on fit, the stage: {}"

    def __init__(self, stage, **kwargs):
        self._stage = stage
        desc = FitOnly._FITONLY_DESC.format(stage.description())
        super_kwargs = {
            "desc": desc,
        }
        super_kwargs.update(**kwargs)
        super().__init__(**super_kwargs)

    def _prec(self, X):
        if self.is_fitted:
            return True
        return self._stage._prec(X)

    def _fit_transform(self, X, verbose):
        self.is_fitted = True
        return self._stage.fit_transform(X, verbose=verbose)

    def _transform(self, X, verbose):
        if verbose:
            print(
                f"Skipping, because not in fit, "
                f"the stage: {self._stage.description()}"
            )
        return X
Last update: 2022-01-23