ssm-simulators 0.11.3

SSMS is a package collecting simulators and training data generators for cognitive science, neuroscience, and approximate bayesian computation

SSMS (Sequential Sampling Model Simulators)

Python Package to collect simulators for Sequential Sampling Models.

Find the package documentation here.

Quick Start

The ssms package serves two purposes.

1. Easy access to fast simulators of sequential sampling models
2. Support infrastructure to construct training data for various approaches to likelihood / posterior amortization

A number of tutorial notebooks are available under the /notebooks directory.

Installation

pip install ssm-simulators

[!NOTE] Building from source or developing this package requires a C compiler (such as GCC). On Linux, you can install GCC with:

sudo apt-get install build-essential

Most users installing from PyPI wheels do not need to install GCC.

Command Line Interface

The package exposes a command-line tool, generate, for creating training data from a YAML configuration file.

generate --config-path <path/to/config.yaml> --output <output/directory> [--log-level INFO]

• --config-path: Path to your YAML configuration file (required).
• --output: Directory where generated data will be saved (required).
• --n-files: (Optional) Number of data files to generate. Default is 1 file.
• --log-level: (Optional) Set the logging level (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default is WARNING.

Below is a sample YAML configuration you can use with the generate command:

MODEL: 'ddm'
N_SAMPLES: 2000
N_PARAMETER_SETS: 100
DELTA_T: 0.001
N_TRAINING_SAMPLES_BY_PARAMETER_SET: 200
N_SUBRUNS: 20
GENERATOR_APPROACH: 'lan'

Configuration file parameter details follow.

Option	Definition
MODEL	The type of model you want to simulate
N_SAMPLES	Number of samples a simulation run should entail for a given parameter set
N_PARAMETER_SETS	Number of parameter vectors that are used for training
DELTA_T	Time discretization step used in numerical simulation of the model. Interval between updates of evidence-accumulation.
N_TRAINING_SAMPLES_BY_PARAMETER_SET	Number of times the kernal density estimate (KDE) is evaluated after creating the KDE from simulations of each set of model parameters.
N_SUBRUNS	Number of repetitions of each call to generate data
GENERATOR_APPROACH	Type of generator used to generate data

To make your own configuration file, you can copy the example above into a new .yaml file and modify it with your preferences.

If you are using uv (see below), you can use the uv run command to run generate from the command line

This will generate training data according to your configuration and save it in the specified output directory.

Tutorial

Check the basic tutorial here.

Advanced: Dependency Management with uv

We use uv for fast and efficient dependency management. To get started:

1. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install dependencies (including development):

uv sync --all-groups  # Installs all dependency groups

Cite ssm-simulators

Please use the this DOI to cite ssm-simulators: https://doi.org/10.5281/zenodo.17156205