Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Overview

Companion code for "Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees"

Installation

We use pip to install things into a python virtual environment. Refer to requirements.txt for package requirements. We use nestly + SCons to run simulations.

File descriptions

generate_data_single_pop.py -- Simulate a data stream from a single population following a logistic regression model.

  • Inputs:
    • --simulation: string for selecting the type of distribution shift. Options for this argument are the keys in SIM_SETTINGS in constants.py.
  • Outputs:
    • --out-file: pickle file containing the data stream

generate_data_two_pop.py -- Simulate a data stream from two subpopulations, where each are generated using logistic regression models. Similar arguments as generate_data_single_pop.py. The percentage split beween the two subpopulations is controlled by the --subpopulations argument.

  • Outputs:
    • --out-file: pickle file containing the data stream

create_modeler.py -- Creates a model developer who fits the original prediction model and may propose a continually refitted model at each time point.

  • Inputs:
    • --data-file: pickle file with the entire data stream
    • --simulation: string for selecting the model refitting strategy by the model developer. Options are to keep the model locked (locked), refit on all accumulated data (cumulative_refit), and refit on the latest observations within some window length (boxed, window length specified by --max-box). The last two options is to train an ensemble with the original and the cumulative_refit models (combo_refit) and train an ensemble with the original and the boxed models (combo_boxed).
  • Outputs:
    • --out-file: pickle file containing the modeler

main.py -- Given the data and the model developer, run online model recalibration/revision using MarBLR and BLR.

  • Inputs:
    • --data-file: pickle file with the entire data stream
    • --model-file: pickle file with the model developer
    • --type-i-regret-factor: Type I regret will be controlled at the rate of args.type_i_regret_factor * (Initial loss of the original model)
    • --reference-recalibs: comma-separated string to select which other online model revisers to run. Options are no updating at all locked, ADAM adam, cumulative logistic regression cumulativeLR.
  • Outputs:
    • --obs-scores-file: csv file containing predicted probabilities and observed outcomes on the data stream
    • --history-file: csv file containing the predicted and actual probabilities on a held-out test data stream (only available if the data stream was simulated)
    • --scores-file: csv file containing performance measures on a held-out test data stream (only available if the data stream was simulated)
    • --recalibrators-file: pickle file containing the history of the online model revisers

Reproducing simulation results

The simulation_recalib folder contains the first set of simulations for online model recalibration. The simulation_revise folder contains the second set of simulations where we perform online logistic revision. The simulation_revise folder contains the third set of simulations where we perform online ensembling of the original model with a continually refitted model. The copd_analysis folder contains code for online model recalibration and revision for the COPD dataset. To reproduce the simulations, run scons .

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