[FIXED] Combine two fitted estimators into a pipeline

Issue

I have data at two stages:

import numpy as np

data_pre = np.array([[1., 2., 203.],
                     [0.5, np.nan, 208.]])

data_post = np.array([[2., 2., 203.],
                      [0.5, 2., 208.]])

I also have two pre-existing fitted estimators:

from sklearn.preprocessing import Imputer
from sklearn.ensemble import GradientBoostingRegressor

imp = Imputer(missing_values=np.nan, strategy='mean', axis=1).fit(data_pre)
gbm = GradientBoostingRegressor().fit(data_post[:,:2], data_post[:,2])

I need to pass a fitted pipeline and data_pre to another function.

def the_function_i_need(estimators):
    """
    """
    return fitted pipeline

fitted_pipeline = the_function_i_need([imp, gbm])
sweet_output = static_function(fitted_pipeline, data_pre) 

Is there a way to combine these two existing and fitted model objects into a fitted pipeline without refitting the models or am I out of luck?

Solution

...A few years later. Use make_pipeline() to concatenate scikit-learn estimators as:

new_model = make_pipeline(fitted_preprocessor,
                          fitted_model)

Answered By - Cristobal