preprocessors.py

import numpy as np
import pandas as pd

from sklearn.base import BaseEstimator, TransformerMixin



class TemporalVariableTransformer(BaseEstimator, TransformerMixin):
	# Temporal elapsed time transformer

    def __init__(self, variables, reference_variable):
        
        if not isinstance(variables, list):
            raise ValueError('variables should be a list')
        
        self.variables = variables
        self.reference_variable = reference_variable

    def fit(self, X, y=None):
        # we need this step to fit the sklearn pipeline
        return self

    def transform(self, X):

    	# so that we do not over-write the original dataframe
        X = X.copy()
        
        for feature in self.variables:
            X[feature] = X[self.reference_variable] - X[feature]

        return X



# categorical missing value imputer
class Mapper(BaseEstimator, TransformerMixin):

    def __init__(self, variables, mappings):

        if not isinstance(variables, list):
            raise ValueError('variables should be a list')

        self.variables = variables
        self.mappings = mappings

    def fit(self, X, y=None):
        # we need the fit statement to accomodate the sklearn pipeline
        return self

    def transform(self, X):
        X = X.copy()
        for feature in self.variables:
            X[feature] = X[feature].map(self.mappings)

        return X