# Copyright (c) 2022 The BayesFlow Developers
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
# Corresponds to Task T.8 from the paper https://arxiv.org/pdf/2101.04653.pdf
import numpy as np
bayesflow_benchmark_info = {
"simulator_is_batched": False,
"parameter_names": [r"$\theta_1$", r"$\theta_2$"],
"configurator_info": "posterior",
}
[docs]
def prior(lower_bound=-1.0, upper_bound=1.0, rng=None):
"""Generates a random draw from a 2-dimensional uniform prior bounded between
`lower_bound` and `upper_bound` which represents the two parameters of the two moons simulator.
Parameters
----------
lower_bound : float, optional, default : -1
The lower bound of the uniform prior.
upper_bound : float, optional, default : 1
The upper bound of the uniform prior.
rng : np.random.Generator or None, default: None
An optional random number generator to use.
Returns
-------
theta : np.ndarray of shape (2,)
A single draw from the 2-dimensional uniform prior.
"""
if rng is None:
rng = np.random.default_rng()
return rng.uniform(low=lower_bound, high=upper_bound, size=2)
[docs]
def simulator(theta, rng=None):
"""Implements data generation from the two-moons model with a bimodal posterior.
See https://arxiv.org/pdf/2101.04653.pdf, Benchmark Task T.8
Parameters
----------
theta : np.ndarray of shape (2,)
The vector of two model parameters.
rng : np.random.Generator or None, default: None
An optional random number generator to use.
Returns
-------
x : np.ndarray of shape (2,)
The 2D vector generated from the two moons simulator.
"""
# Use default RNG, if None specified
if rng is None:
rng = np.random.default_rng()
# Generate noise
alpha = rng.uniform(low=-0.5 * np.pi, high=0.5 * np.pi)
r = rng.normal(loc=0.1, scale=0.01)
# Forward process
rhs1 = np.array([r * np.cos(alpha) + 0.25, r * np.sin(alpha)])
rhs2 = np.array([-np.abs(theta[0] + theta[1]) / np.sqrt(2.0), (-theta[0] + theta[1]) / np.sqrt(2.0)])
return rhs1 + rhs2
[docs]
def configurator(forward_dict, mode="posterior"):
"""Configures simulator outputs for use in BayesFlow training."""
# Case only posterior configuration
if mode == "posterior":
input_dict = _config_posterior(forward_dict)
# Case only plikelihood configuration
elif mode == "likelihood":
input_dict = _config_likelihood(forward_dict)
# Case posterior and likelihood configuration (i.e., joint inference)
elif mode == "joint":
input_dict = {}
input_dict["posterior_inputs"] = _config_posterior(forward_dict)
input_dict["likelihood_inputs"] = _config_likelihood(forward_dict)
# Throw otherwise
else:
raise NotImplementedError('For now, only a choice between ["posterior", "likelihood", "joint"] is available!')
return input_dict
def _config_posterior(forward_dict):
"""Helper function for posterior configuration."""
input_dict = {}
input_dict["parameters"] = forward_dict["prior_draws"].astype(np.float32)
input_dict["direct_conditions"] = forward_dict["sim_data"].astype(np.float32)
return input_dict
def _config_likelihood(forward_dict):
"""Helper function for likelihood configuration."""
input_dict = {}
input_dict["conditions"] = forward_dict["prior_draws"].astype(np.float32)
input_dict["observables"] = forward_dict["sim_data"].astype(np.float32)
return input_dict
