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PEtab import: Priors aren't support -> raise #384

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Sep 16, 2024
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PEtab import: Priors aren't support -> raise #384

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470b468
PEtab import: Priors aren't support -> raise
dweindl Sep 16, 2024
083e9e4
Skip unsupported benchmark models
dweindl Sep 16, 2024
d92b8c5
Revert "Skip unsupported benchmark models"
dweindl Sep 16, 2024
1e0b741
Skip unsupported benchmark models
dweindl Sep 16, 2024
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5 changes: 3 additions & 2 deletions benchmark_collection/all.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -13,7 +13,6 @@ Borghans_BiophysChem1997
Crauste_CellSystems2017
Elowitz_Nature2000
Fujita_SciSignal2010
Schwen_PONE2014
Sneyd_PNAS2002
Weber_BMC2015
Zheng_PNAS2012
Expand Down Expand Up @@ -47,7 +46,9 @@ Zheng_PNAS2012
# Brannmark_JBC2010 FAILED: Expected llh 283.778227541074, got nllh 141.889034
# Lucarelli_CellSystems2018
# Fiedler_BMC2016 FAILED: Expected llh -117.16780323362, got nllh 109391.496205

#
# Unsupported priors:
# Schwen_PONE2014
for model_name in $expected_to_work; do
printf '=%.0s' {1..20}
printf %s "${model_name}"
Expand Down
25 changes: 22 additions & 3 deletions python/parpe/hdf5_pe_input.py
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Expand Up @@ -93,9 +93,6 @@ def __init__(self,
self.petab_problem: petab.Problem = petab_problem
self.amici_model: amici.Model = amici_model

# ensure we have valid inputs
petab.lint_problem(self.petab_problem)

# index for no reference/preequilibration condition
self.NO_PREEQ_CONDITION_IDX: int = NO_PREEQ_CONDITION_IDX

Expand All @@ -105,6 +102,28 @@ def __init__(self,
# hdf5 dataset compression
self.compression = "gzip"

self._check_support()

def _check_support(self):
"""
Check if the model is supported by parPE
"""
# ensure we have valid inputs
petab.lint_problem(self.petab_problem)

# We can't handle priors yet
# Initialization priors would work in principle when we sample
# starting points using petab. However, the current parpe C++
# resampling would ignore the initialization prior.
# And they wouldn't be supported for hierarchical optimization.
parameter_df = self.petab_problem.parameter_df
for col_id in [
ptc.INITIALIZATION_PRIOR_TYPE, ptc.INITIALIZATION_PRIOR_PARAMETERS,
ptc.OBJECTIVE_PRIOR_TYPE, ptc.OBJECTIVE_PRIOR_PARAMETERS
]:
if (col := parameter_df.get(col_id)) is not None and col.notna().any():
raise NotImplementedError("Priors are not supported yet.")

def generate_file(self, hdf5_file_name: str) -> None:
"""
Create the output file
Expand Down