splearn/pipeline.py
def fit_transform(self, Z, **fit_params):
        """Fit all the transforms one after the other and transform the
        data, then use fit_transform on transformed data using the final
        estimator."""
        Zt, fit_params = self._pre_transform(Z, **fit_params)
        if hasattr(self.steps[-1][-1], 'fit_transform'):
            return self.steps[-1][-1].fit_transform(Zt, **fit_params)
        else:
            return self.steps[-1][-1].fit(Zt, **fit_params).transform(Zt)
def fit_transform(self, Z, **fit_params):
        Zt, fit_params = self._pre_transform(Z, **fit_params)
        if hasattr(self.steps[-1][-1], 'fit_transform'):
            return self.steps[-1][-1].fit_transform(Zt, **fit_params)
        else:
            return self.steps[-1][-1].fit(Zt, **fit_params).transform(Zt)
def fit_transform ( self , Z , * * fit_params ) : Zt , fit_params = self . _pre_transform ( Z , * * fit_params ) if hasattr ( self . steps [ - 1 ] [ - 1 ] , 'fit_transform' ) : return self . steps [ - 1 ] [ - 1 ] . fit_transform ( Zt , * * fit_params ) else : return self . steps [ - 1 ] [ - 1 ] . fit ( Zt , * * fit_params ) . transform ( Zt )
Fit all the transforms one after the other and transform the
        data, then use fit_transform on transformed data using the final
        estimator.
Fit all the transforms one after the other and transform the data then use fit_transform on transformed data using the final estimator .