PyIBS: Inverse Binomial Sampling in Python

What is it?

PyIBS is a a Python implementation of the Inverse Binomial Sampling (IBS) estimator for obtaining unbiased, efficient estimates of the log-likelihood of a model by simulation, originally implemented in MATLAB. [1]

When should I use PyIBS?

The typical scenario is the case in which you have a simulator, that is a model from which you can randomly draw synthetic observations (for a given parameter vector), but cannot evaluate the log-likelihood analytically or numerically. In other words, IBS affords likelihood-based inference for models without explicit likelihood functions (also known as implicit models).

IBS is commonly used as a part of an algorithm for maximum-likelihood estimation or Bayesian inference.

• For maximum-likelihood (or maximum-a-posteriori) estimation, we recommend to use PyIBS combined with Bayesian Adaptive Direct Search (PyBADS).
• For Bayesian inference of posterior distributions and model evidence, we recommend to use PyIBS with Variational Bayesian Monte Carlo (PyVBMC).

This folder contains Python implementations and examples of IBS.

Installation

PyIBS is available via pip and conda-forge.

1. Install with: console python -m pip install pyibs or: console conda install --channel=conda-forge pyibs PyIBS requires Python version 3.9 or newer.

Quick start

The typical workflow of PyIBS follows four steps:

1. Define the model function (a generative function from which you can draw synthetic observations);
2. Setup the problem configuration (response matrix, stimulus matrix);
3. Initialize and then run the estimator OR use the estimator as objective function in an optimization (for example Bayesian Adaptive Direct Search (PyBADS) or Variational Bayesian Monte Carlo (PyVBMC));
4. Examine and visualize the results.

Initializing and running the estimator in step 3 only involves a couple of lines of code:

``` from pyibs import IBS

...

ibs = IBS(samplefrommodel, responsematrix, designmatrix) neglogl = ibs(params, numreps, additionaloutput, returnpositive) ```

with input arguments for the initialization:

• sample_from_model: model function, it takes as input a vector of parameters params and design_matrix and generates a matrix of simulated model responses (one row per trial, corresponding to rows of design_matrix);
• response_matrix: the observed responses;
• design_matrix: used as input to sample from the model; and following optional input arguments:
• vectorized: indicates whether to use a vectorized sampling algorithm with acceleration. If it is not given, the vectorized algorithm is used if the time to generate samples for each trial is less than vectorized_threshold.
• acceleration: acceleration factor for vectorized sampling, default = 1.5;
• num_samples_per_call: number of starting samples per trial per function call. If equal to 0 the number of starting samples is chosen automatically, default = 0;
• max_iter: maximum number of iterations (per trial and estimate), default = 1e5;
• max_time: maximum time for an IBS call (in seconds), default = np.inf;
• max_samples: maximum number of samples per function call, default = 1e4;
• acceleration_threshold: threshold at which to stop accelerating (in seconds), default = 0.1;
• vectorized_threshold: maximum threshold for using the vectorized algorithm (in seconds), default = 0.1;
• max_mem: maximum number of samples for the vectorized implementation, default = 1e6;
• neg_logl_threshold: threshold for the negative log-likelihood (works differently in vectorized version), default = np.inf.

Input arguments for running the estimator:

• params: parameter vector used to simulate the model's responses;
• num_reps: number of independent log-likelihood estimates to calculate, an average of the repetitions is returned. If not given, it is 10;
• additional_output: The output type, if not given then only the negative log-likelihood is returned. If equal to:
  • var then the negative log-likelihood and the variance of the negative log-likelihood estimate is returned,
  • std then the negative log-likelihood and the standard deviation of the negative log-likelihood estimate is returned,
  • full then a dictionary type output is returned with additional information about the estimate;
• return_positive: boolean that indicates whether to return the positive log-likelihood. If not given, the negative log-likelihood estimate is returned;

The outputs are:

• neg_logl: the negative log-likelihood (if return_positive is False else positive log-likelihood);
• neg_logl_var: the variance of the negative log-likelihood estimate (if additional_output is var);
• neg_logl_std: the standard deviation of negative log-likelihood estimate (if additionaloutput is std); If ``additionaloutputisfull`` then a dictionary type output is returned with following additional information about the sampling:
• exit_flag: the exit flag (0 = correct termination, 1 = negative log-likelihood threshold reached, 2 = maximum runtime reached, 3 = maximum iterations reached);
• message: the exit message;
• elapsed_time: the elapsed time (in seconds);
• num_samples_per_trial: the number of samples per trial;
• fun_count: the number of sample_from_model function evaluations in the call;

Code

• ibs_basic.py is a bare-bone implementation of IBS for didactic purposes.
• psycho_generator.py and psycho_neg_logl.py are functions implementing, respectively, the generative model (simulator) and the negative log-likelihood function for the orientation discrimination model used in the example notebooks.
• ibs.py is an advanced vectorized implementation of IBS, which supports several advanced features: it allows for repeated sampling, early stopping through a log-likelihood threshold and to return variance or standard deviation of the estimation.
  • Initialize an IBS object by passing it a generative model, a response matrix and a design matrix. Call the object with a parameter to return an estimate of the negative log-likelihood.
  • Note that by default it returns the negative log-likelihood as it is meant to be used with an optimization method such as PyBADS. Set return_positive = true to return the positive log-likelihood.
  • If you want to run with PyVBMC, note that you need to pass the following arguments when calling the IBS object
  • return_positive = true to return the positive log-likelihood;
  • additinal_output = std to return as second output the standard deviation of the estimate.
• ibs_simple_example.ipynb is an example notebook for running ibs_basic.py. It is only for didactic purposes.
• ibs_example_1_basic_use.ipynb is an example notebook for running ibs.py. It contains an example on how to run the estimations and how to obtain different output types. It contains examples using the orientation discrimination model and one using a binomial model. The unbiasedness of the estimator is checked; this notebook is only for didactic purposes.
• ibs_example_2_parameter_estimation.ipynb is a full working example usage of IBS. It requires the installation of PyBADS and PyVBMC.

References

1. van Opheusden*, B., Acerbi*, L. & Ma, W.J. (2020). Unbiased and efficient log-likelihood estimation with inverse binomial sampling. PLoS Computational Biology 16(12): e1008483. (* equal contribution) (link)