pipeline_elements.py [download]

#!/usr/bin/env python3


################################################################
#
# These custom classes help with pipeline building and debugging
#
import sklearn.base

class PipelineNoop(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin):
    """
    Just a placeholder with no actions on the data.
    """
    
    def __init__(self):
        return

    def fit(self, X, y=None):
        self.is_fitted_ = True
        return self

    def transform(self, X, y=None):
        return X

class Printer(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin):
    """
    Pipeline member to display the data at this stage of the transformation.
    """
    
    def __init__(self, title):
        self.title = title
        return

    def fit(self, X, y=None):
        self.is_fitted_ = True
        return self

    def transform(self, X, y=None):
        print("{}::type(X)".format(self.title), type(X))
        print("{}::X.shape".format(self.title), X.shape)
        if not isinstance(X, pd.DataFrame):
            print("{}::X[0]".format(self.title), X[0])
        print("{}::X".format(self.title), X)
        return X

class DataFrameSelector(sklearn.base.BaseEstimator, sklearn.base.TransformerMixin):
    
    def __init__(self, do_predictors=True, do_numerical=True):
        # skip "name", "ticket", "cabin", "boat", "body", "home.dest"
        self.mCategoricalPredictors = ["pclass", "sex", "embarked"]
        self.mNumericalPredictors = ["age", "sibsp", "parch", "fare", ]
        self.mLabels = ["survived"]
        self.do_numerical = do_numerical
        self.do_predictors = do_predictors
        
        if do_predictors:
            if do_numerical:
                self.mAttributes = self.mNumericalPredictors
            else:
                self.mAttributes = self.mCategoricalPredictors                
        else:
            self.mAttributes = self.mLabels
            
        return

    def fit( self, X, y=None ):
        # no fit necessary
        self.is_fitted_ = True
        return self

    def transform( self, X, y=None ):
        # only keep columns selected
        values = X[self.mAttributes]
        return values

#
# These custom classes help with pipeline building and debugging
#
################################################################

Last Updated 02/12/2025