Custom Classes for Pipeline

custom_pipeline_1.py

from sklearn.ensemble import BaseEnsemble

# Build custom classes to add to the pipeline

class SelectColumnsTransformer(BaseEnsemble):
    
    def __init__(self, columns=None):
        self.columns = columns
        
    def transform(self, X, **transform_params):
        cpy_df = X[self.columns].copy()
        return cpy_df

    def fit(self, X, y=None, **fit_params):
        return self
    
    
class Transform_Categorical(BaseEnsemble):
    
    def transform(self, X, y=None, **transform_params):
        try:
            X['international plan'] = X['international plan'].apply(self.yes_no_func)
            X['voice mail plan'] = X['voice mail plan'].apply(self.yes_no_func)
        except:
            pass
        return X

    def fit(self, X, y=None, **fit_params):
        return self
    
    @staticmethod
    def yes_no_func(x):
        return 1 if x.lower() == 'yes' else 0