GPy-ABCD 1.2.3

Basic implementation with GPy of an Automatic Bayesian Covariance Discovery (ABCD) system

(Temporary note: the “failing” build badge above is due to the workflow pip not finding GPy 1.12.0 for some reason; the tests are successful)

GPy-ABCD is a basic implementation with GPy of an Automatic Bayesian Covariance Discovery (ABCD) system.

Briefly, ABCD is a (Gaussian Process) modelling method which consists in exploring a space of modular kernels (i.e. covariances) constructed by iteratively combining a small set of simple ones, and returning the best fitting models using them; due to its modularity, it is capable of generating simple text descriptions of the fits based on the identified functional shapes.

The usefulness of ABCD is in identifying the underlying shape of data, but the process is computationally expensive, therefore a typical use for it is in initial data overviews (possibly on subsampled datasets for efficiency), then followed by more direct exploration of its top few results’ kernels (on the full dataset if subsampled before).

See the picture in Usage below for an example input/output and read the paper for further details:

Fletcher, T., Bundy, A., & Nuamah, K. . GPy-ABCD: A Configurable Automatic Bayesian Covariance Discovery Implementation. 8th ICML Workshop on Automated Machine Learning (2021)

Installation

pip install GPy_ABCD

Usage

The main function exported by this package is explore_model_space; see its description for parameter information and type hints. Note that with the default model_list_fitter = fit_mods_parallel_processes argument the function should be called from within a if __name__ == '__main__': for full OS-agnostic use.

A minimal example to showcase the various parameters follows:

import numpy as np
from GPy_ABCD import *


if __name__ == '__main__':
    # Example data
    X = np.linspace(-10, 10, 101)[:, None]
    Y = np.cos((X - 5) / 2) ** 2 * X * 2 + np.random.randn(101, 1)

    # Main function call with default arguments
    best_mods, all_mods, all_exprs, expanded, not_expanded = explore_model_space(X, Y,
        start_kernels = start_kernels['Default'], p_rules = production_rules['Default'],
        utility_function = BIC, rounds = 2, beam = [3, 2, 1], restarts = 5,
        model_list_fitter = fit_mods_parallel_processes,
        optimiser = GPy_optimisers[0], max_retries = 1, verbose = True)

    print('\nFull lists of models by round:')
    for mod_depth in all_mods: print(', '.join([str(mod.kernel_expression) for mod in mod_depth]) + f'\n{len(mod_depth)}')

    print('\n\nTop-3 models\' details:')
    for bm in best_mods[:3]:
        model_printout(bm, plotly = False) # See the definition of this convenience function for examples of model details' extraction
        print('Prediction at X = 11:', bm.predict(np.array([11])[:, None]), '\n')

    from matplotlib import pyplot as plt
    plt.show() # Not required for plotly = True above

    # Later fit one of the top kernels to new data (e.g. if the original data was subsampled)
    #fit_kex(other_X, other_Y, best_mods[0].kernel_expression, 10)

Selection of output from the above example

The directly importable elements from this package are essentially those required to customise any of the arguments of the main model search function plus a few convenient tools (refer to the section below for context):

• The main function explore_model_space

• The model_search_rounds function to continue a search from where another left-off

• Functions to be used as model_list_fitter argument: fit_mods_not_parallel and fit_mods_parallel_processes (using multiprocessing’s Pool)

• The single-fit function fit_one_model, on which the list-fitting functions above are built (so that a user may implement their preferred parallelisation)

• Non-searching single-fit functions: fit_kex (which takes KernelExpression inputs and simplifies them if required) and fit_GPy_kern (which takes GPy kernel inputs and returns GPy GPRegression objects, not full GPy-ABCD GPModel ones)

• A few standard model-scoring functions: BIC, AIC, AICc

• The aforementioned convenience function model_printout

• The list of GPy 1.9.9 model fit optimisers GPy_optimisers

• A few (named) choices of start kernels and production rules in the dictionaries start_kernels and production_rules

• The dictionary of available production rules grouped by type production_rules_by_type

• The concrete KernelExpression subclasses SumKE, ProductKE and ChangeKE

• The frozensets of base_kerns and base_sigmoids

(The purpose of exporting elements in the last 3 lines is for users to create alternative sets of production rules and starting kernels lists by mixing kernel expressions and raw strings of base kernels; see the definitions of entries of the start_kernels and production_rules dictionaries for examples)

Project Structure

Briefly, the ABCD process consists in exploring a space of compositional kernels built from a few carefully selected base ones, returning the best fitting models using them and generating simple text interpretations of the fits based on the functional shapes of the final composed covariance kernels and parameter values.

The core components of this project’s ABCD implementation are the following:

• Kernels.baseKernels contains the “mathematical” base kernels (i.e. GPy kernel objects) for the whole machinery

  • Some of the base kernels are simply wrapped GPy-provided kernels (White-Noise, Constant and Squared-Exponential)

  • The others are either not present in GPy’s default arsenal or are improved versions of ones which are (Linear which can identify polynomial roots and purely-Periodic standard-periodic kernel)

  • It contains sigmoidal kernels (both base sigmoids and indicator-like ones, i.e. sigmoidal hat/well) which are not used directly in the symbolic expressions but are substituted in by change-type kernels

  • It contains change-point and change-window kernels which use the aforementioned sigmoidals

• KernelExpression contains the “symbolic” kernel classes constituting the nodes with which to build complex kernel expressions in the form of trees

  • The non-abstract kernel expression classes are SumKE, ProductKE and ChangeKE

  • SumKE and ProductKE are direct subclasses of the abstract class SumOrProductKE and only really differ in how they self-simplify and distribute over the other

  • ChangeKE could be split into separate change-point and change-window classes, but a single argument difference allows full method overlap

  • SumOrProductKE and ChangeKE are direct subclasses of the abstract base class KernelExpression

• The above kernel expression classes have a wide variety of methods providing the following general functionality in order to make the rest of the project light of ad-hoc functions:

  • They self-simplify when modified through the appropriate methods (they are symbolic expressions after all)

  • They can produce GPy kernel objects

  • They can line-up with and absorb fit model parameters from a matching GPy object

  • They can rearrange to a sum-of-products form

  • They can generate text interpretations of their sum-of-products form

• KernelExpansion.grammar contains the various production rules and default starting kernel lists used in model space exploration

• Models.modelSearch contains the system front-end elements:

  • The GPModel class, which is where the GPy kernels/models interact with the symbolic kernel expressions

  • The aforementioned functions to fit lists of models fit_mods_not_parallel and fit_mods_parallel_processes

  • The explore_model_space function, which is the point of it all

  • The model_search_rounds function, which is used by the above but also meant to continue searching by building on past exploration results

Note: a config.py file is present, and it contains a few global-behaviour-altering flags (e.g. enabling/disabling the Squared-Exponential kernel)

Further Notes

Generic:

• Please reach out if you have successfully used this project in your own research

• Feel free to fork and expand this project (pull requests are welcome) since it was only written because its particular features were required in a broader adaptive statistical modelling context, and therefore it is unlikely that its functionality will be expanded in the near future

Code-related:

• The important tests are in pytest scripts, but many other scripts are present and intended as functionality showcases or “tests by inspection”

• Additionally, pytest.ini has a two opposite configuration lines intended to be toggled to perform “real” tests vs other “by inspection” tests

Possible expansion directions:

• Many “TODO” comments are present throughout the codebase

• Optimising ChangeWindow window-location fitting is an open issue (multiple implementations of change-window and the sigmoidal kernels they rely on have already been tried; see the commented-out declarations in baseKernels.py inv ersions before v1.0)

• The periodic kernel could be more stable in non-periodic-data fits (GPy’s own as well)

• Making each project layer accept multidimensional data, starting from the GPy kernels (some already do)

• Expanding on the GPy side of things: add more methods to the kernels in order to make use of the full spectrum of GPy features (MCMC etc)