Install pylift:
pip install pylift
To start, you simply need a pandas.DataFrame with a treatment column of 0s and 1s (0 for control, 1 for test) and a numeric outcome column. Implementation can be as simple as follows:
from pylift import TransformedOutcome
up = TransformedOutcome(df1, col_treatment='Treatment', col_outcome='Converted')
up.randomized_search()
up.fit(**up.rand_search_.best_params_)
up.plot(plot_type='aqini', show_theoretical_max=True)
print(up.test_results_.Q_aqini)
up.fit() can also be passed a flag productionize=True, which when True will create a productionizable model trained over the entire data set, stored in self.model_final (though it is contentious whether it's safe to use a model that has not been evaluated in production -- if you have enough data, it may be prudent not to). This can then be pickled with self.model_final.to_pickle(PATH), as usually done with sklearn-style models.
A fully-fledged example of pylift in action can be found in pylift/examples/simulated_data/sample.ipynb.