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Unbounded priors (#103)
* Add test for unbounded priors * Update test case 0024 description
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‎petabtests/cases/v2.0.0/sbml/0024/0024.py‎

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case = PetabV2TestCase.from_problem(
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id=24,
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brief="Prior distributions.",
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brief="Truncated prior distributions.",
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description=DESCRIPTION,
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model=sbml_file,
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problem=problem,

‎petabtests/cases/v2.0.0/sbml/0025/0025.py‎

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from inspect import cleandoc
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from numpy import inf
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from petab.v2.C import *
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from petab.v2 import Problem
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from petabtests import (
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PetabV2TestCase,
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antimony_to_sbml_str,
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)
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from pathlib import Path
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from petab.v2 import PriorDistribution
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DESCRIPTION = cleandoc(r"""
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## Objective
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This case tests different non-truncated prior distributions, as well as
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implicit uniform priors and fixed parameters.
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## Model
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A simple model with all constant parameters.
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""")
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# problem --------------------------------------------------------------------
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priors = [
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(PriorDistribution.UNIFORM, (2, 8), 2, 8),
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(PriorDistribution.NORMAL, (4, 2), -inf, inf),
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(PriorDistribution.LOG_NORMAL, (5, 2), 0.0, inf),
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(PriorDistribution.CAUCHY, (3, 5), -inf, inf),
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(PriorDistribution.CHI_SQUARED, (4), 0.0, inf),
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(PriorDistribution.EXPONENTIAL, (3), 0.0, inf),
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(PriorDistribution.GAMMA, (3, 5), 0, inf),
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(PriorDistribution.LAPLACE, (3, 5), -inf, inf),
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(PriorDistribution.LOG_LAPLACE, (3, 5), 0, inf),
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(PriorDistribution.LOG_UNIFORM, (3, 5), 3, 5),
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(PriorDistribution.RAYLEIGH, (3), 0, inf),
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]
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tested_prior_distrs = {pd for pd, _, _, _ in priors}
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untested_distrs = [
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pd.value for pd in PriorDistribution if pd not in tested_prior_distrs
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]
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if untested_distrs:
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print("Untested prior distributions:", untested_distrs)
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sbml_file = Path(__file__).parent / "_model.xml"
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parameters = "\n".join(
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f"p_{prior_type.value.replace('-', '_')} = 5;" for prior_type, _, _, _ in priors
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)
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ant_model = f"""
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model petab_test_0025
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{parameters}
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end
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"""
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sbml_file.write_text(antimony_to_sbml_str(ant_model))
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problem = Problem()
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for prior_type, prior_pars, support_lb, support_ub in priors:
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problem.add_parameter(
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f"p_{prior_type.value.replace('-', '_')}",
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estimate=True,
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lb=support_lb,
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ub=support_ub,
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nominal_value=5,
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prior_distribution=prior_type,
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prior_parameters=prior_pars,
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)
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# implicit uniform prior
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problem.add_parameter("p1", estimate=True, nominal_value=1, lb=0, ub=2)
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# fixed, i.e., no prior
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problem.add_parameter("p_fixed", estimate=False, nominal_value=1)
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# we need some observable and measurement
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problem.add_observable("obs_p1", "p1", noise_formula="p_fixed")
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problem.add_measurement("obs_p1", experiment_id="", time=0, measurement=1)
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# solutions ------------------------------------------------------------------
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simulation_df = problem.measurement_df.copy(deep=True).rename(
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columns={MEASUREMENT: SIMULATION}
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)
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simulation_df[SIMULATION] = [
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1,
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]
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case = PetabV2TestCase.from_problem(
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id=25,
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brief="Non-truncated prior distributions.",
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description=DESCRIPTION,
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model=sbml_file,
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problem=problem,
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simulation_df=simulation_df,
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)

‎petabtests/cases/v2.0.0/sbml/0025/README.md‎

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# PEtab test case 0025
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## Objective
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This case tests different non-truncated prior distributions, as well as
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implicit uniform priors and fixed parameters.
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## Model
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A simple model with all constant parameters.

‎petabtests/cases/v2.0.0/sbml/0025/_0025.yaml‎

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condition_files: []
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experiment_files: []
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format_version: 2.0.0
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measurement_files:
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- _measurements.tsv
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model_files:
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model_0:
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language: sbml
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location: _model.xml
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observable_files:
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- _observables.tsv
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parameter_files:
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- _parameters.tsv

‎petabtests/cases/v2.0.0/sbml/0025/_0025_solution.yaml‎

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chi2: 0.0
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llh: -0.91893853320467
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log_prior:
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p1: -0.69314718055995
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p_cauchy: -2.90258780340177
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p_chisquare: -2.27685644868579
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p_exponential: -2.76527895533478
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p_gamma: -3.30258509299405
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p_laplace: -2.70258509299405
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p_log_laplace: -4.19013542294133
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p_log_normal: -4.65851253490362
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p_log_uniform: -0.93771092034198
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p_normal: -1.73708571376462
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p_rayleigh: -1.97667555379101
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p_uniform: -1.79175946922805
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simulation_files:
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- _simulations.tsv
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tol_chi2: 0.001
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tol_llh: 0.001
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tol_log_prior: 1.0e-14
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tol_simulations: 0.001
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tol_unnorm_log_posterior: 0.001
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unnorm_log_posterior: -30.85385872214566

‎petabtests/cases/v2.0.0/sbml/0025/_measurements.tsv‎

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modelId observableId experimentId time measurement observableParameters noiseParameters
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obs_p1 0.0 1.0

‎petabtests/cases/v2.0.0/sbml/0025/_model.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<!-- Created by libAntimony version v3.1.1 with libSBML version 5.20.5. -->
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<sbml xmlns="http://www.sbml.org/sbml/level3/version2/core" level="3" version="2">
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<model metaid="petab_test_0025" id="petab_test_0025">
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<listOfParameters>
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<parameter id="p_uniform" value="5" constant="true"/>
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<parameter id="p_normal" value="5" constant="true"/>
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<parameter id="p_log_normal" value="5" constant="true"/>
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<parameter id="p_cauchy" value="5" constant="true"/>
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<parameter id="p_chisquare" value="5" constant="true"/>
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<parameter id="p_exponential" value="5" constant="true"/>
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<parameter id="p_gamma" value="5" constant="true"/>
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<parameter id="p_laplace" value="5" constant="true"/>
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<parameter id="p_log_laplace" value="5" constant="true"/>
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<parameter id="p_log_uniform" value="5" constant="true"/>
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<parameter id="p_rayleigh" value="5" constant="true"/>
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</listOfParameters>
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</model>
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</sbml>

‎petabtests/cases/v2.0.0/sbml/0025/_observables.tsv‎

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observableId observableName observableFormula noiseFormula noiseDistribution observablePlaceholders noisePlaceholders
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obs_p1 p1 p_fixed normal

‎petabtests/cases/v2.0.0/sbml/0025/_parameters.tsv‎

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parameterId lowerBound upperBound nominalValue estimate priorDistribution priorParameters
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p_uniform 2.0 8.0 5.0 true uniform 2.0;8.0
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p_normal -inf inf 5.0 true normal 4.0;2.0
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p_log_normal 0.0 inf 5.0 true log-normal 5.0;2.0
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p_cauchy -inf inf 5.0 true cauchy 3.0;5.0
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p_chisquare 0.0 inf 5.0 true chisquare 4.0
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p_exponential 0.0 inf 5.0 true exponential 3.0
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p_gamma 0.0 inf 5.0 true gamma 3.0;5.0
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p_laplace -inf inf 5.0 true laplace 3.0;5.0
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p_log_laplace 0.0 inf 5.0 true log-laplace 3.0;5.0
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p_log_uniform 3.0 5.0 5.0 true log-uniform 3.0;5.0
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p_rayleigh 0.0 inf 5.0 true rayleigh 3.0
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p1 0.0 2.0 1.0 true
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p_fixed 1.0 false

‎petabtests/cases/v2.0.0/sbml/0025/_simulations.tsv‎

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modelId observableId experimentId time simulation observableParameters noiseParameters
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obs_p1 0.0 1

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