Source code for bayesflow.benchmarks

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# This module implements all 10 benchmark models (tasks) from the paper:
#
# Lueckmann, J. M., Boelts, J., Greenberg, D., Goncalves, P., & Macke, J. (2021).
# Benchmarking simulation-based inference.
# In International Conference on Artificial Intelligence and Statistics (pp. 343-351). PMLR.
#
# https://arxiv.org/pdf/2101.04653.pdf
#
# However, it lifts the dependency on `PyTorch` and implements the models as ready-made
# tuples of prior and simulator functions capable of interacting with BayesFlow.
# Note: All default hyperparameters are set according to the paper.

import importlib
from functools import partial

import numpy as np

from bayesflow.simulation import GenerativeModel, Prior
from bayesflow.exceptions import ConfigurationError


available_benchmarks = [
    'gaussian_linear',
    'gaussian_linear_uniform',
    'slcp',
    'slcp_distractors',
    'bernoulli_glm',
    'bernoulli_glm_raw',
    'gaussian_mixture',
    'two_moons',
    'sir',
    'lotka_volterra'
]


[docs]def get_benchmark_module(benchmark_name): """Loads the corresponding benchmark file under bayesflow.benchmarks.<benchmark_name> as a module and returns it. """ try: benchmark_module = importlib.import_module(f'bayesflow.benchmarks.{benchmark_name}') return benchmark_module except ModuleNotFoundError: raise ConfigurationError(f"You need to provide a valid name from: {available_benchmarks}")
[docs]class Benchmark: """Interface class for a benchmark.""" def __init__(self, name, mode='joint', seed=None, **kwargs): """Creates a benchmark generative model by using the blueprint contained in a benchmark file. Parameters ---------- name : str The name of the benchmark file (without suffix, i.e., .py) to use as a blueprint. mode : str, otpional, default: 'joint' The mode in which to configure the data, should be in ('joint', 'posterior', 'likelihood') seed : int or None, optional, default: None The seed to use if reproducibility is required. Will be passed to a numpy RNG. **kwargs : dict Optional keyword arguments. If 'sim_kwargs' is present, key-value pairs will be interpreted as arguments for the simulator and propagated accordingly. """ self.benchmark_name = name self._rng = np.random.default_rng(seed) self.benchmark_module = get_benchmark_module(self.benchmark_name) self.benchmark_info = getattr(self.benchmark_module, 'bayesflow_benchmark_info') # Prepare partial simulator function with optioal keyword arguments if kwargs.get('sim_kwargs') is not None: _simulator = partial(getattr(self.benchmark_module, 'simulator'), rng=self._rng, **kwargs.get('sim_kwargs')) else: _simulator = partial(getattr(self.benchmark_module, 'simulator'), rng=self._rng) # Prepare generative model self.generative_model = GenerativeModel( prior=Prior( prior_fun=partial(getattr(self.benchmark_module, 'prior'), rng=self._rng), param_names=self.benchmark_info['parameter_names'] ), simulator=_simulator, simulator_is_batched=self.benchmark_info['simulator_is_batched'], name=self.benchmark_name, ) self.configurator = getattr(self.benchmark_module, 'configurator') self.configurator = partial(self.configurator, mode=mode)