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Fix test_priors_to_measurements #336

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Merged
merged 3 commits into from
Dec 9, 2024
Merged

Fix test_priors_to_measurements #336

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749168c
Fix test_priors_to_measurements
dweindl Dec 8, 2024
2aeb015
Update tests/v1/test_priors.py
dweindl Dec 9, 2024
38560d9
Merge branch 'develop' into fix_test_priors_to_measurements
dweindl Dec 9, 2024
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73 changes: 52 additions & 21 deletions tests/v1/test_priors.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,15 @@
from scipy.stats import norm

import petab.v1
from petab.v1 import get_simulation_conditions
from petab.v1 import (
ESTIMATE,
MEASUREMENT,
OBJECTIVE_PRIOR_TYPE,
OBSERVABLE_ID,
SIMULATION,
get_simulation_conditions,
get_simulation_df,
)
from petab.v1.priors import priors_to_measurements


Expand All @@ -17,20 +25,31 @@
)
def test_priors_to_measurements(problem_id):
"""Test the conversion of priors to measurements."""
# setup
petab_problem_priors: petab.v1.Problem = (
benchmark_models_petab.get_problem(problem_id)
)
petab_problem_priors.visualization_df = None
assert petab.v1.lint_problem(petab_problem_priors) is False

if problem_id == "Isensee_JCB2018":
# required to match the stored simulation results below
petab.v1.flatten_timepoint_specific_output_overrides(
petab_problem_priors
)
assert petab.v1.lint_problem(petab_problem_priors) is False

original_problem = deepcopy(petab_problem_priors)
# All priors in this test case are defined on parameter scale, hence
# the dummy measurements will take the scaled nominal values.
x_scaled_dict = dict(
zip(
original_problem.x_free_ids,
original_problem.x_nominal_free_scaled,
strict=True,
)
)
Comment on lines +44 to +50
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Should it really be scaled values here? I assume the prior is either (1) unscaled or (2) on parameterScale. The former means this should be unscaled, and the latter is already handled in the observable formulae for these dummy prior observables, so no scaling is required here?

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So far, this test only handles PARAMETER_SCALE_NORMAL (to be extended in #329). Yes, the scaling is handled in the observable formulae, but here, we need to write the simulation table. For that, we do not evaluate the observable formula using the given parameters, but write the result directly. Therefore, we need the scaled ones.


# convert priors to measurements
petab_problem_measurements = priors_to_measurements(petab_problem_priors)

# check that the original problem is not modified
Expand All @@ -45,6 +64,7 @@ def test_priors_to_measurements(problem_id):
getattr(original_problem, attr)
)
).empty, diff

# check that measurements and observables were added
assert petab.v1.lint_problem(petab_problem_measurements) is False
assert (
Expand All @@ -59,6 +79,7 @@ def test_priors_to_measurements(problem_id):
petab_problem_measurements.measurement_df.shape[0]
> petab_problem_priors.measurement_df.shape[0]
)

# ensure we didn't introduce any new conditions
assert len(
get_simulation_conditions(petab_problem_measurements.measurement_df)
Expand All @@ -67,26 +88,40 @@ def test_priors_to_measurements(problem_id):
# verify that the objective function value is the same

# load/construct the simulation results
simulation_df_priors = petab.v1.get_simulation_df(
simulation_df_priors = get_simulation_df(
Path(
benchmark_models_petab.MODELS_DIR,
problem_id,
f"simulatedData_{problem_id}.tsv",
)
)
simulation_df_measurements = pd.concat(
[
petab_problem_measurements.measurement_df.rename(
columns={petab.v1.MEASUREMENT: petab.v1.SIMULATION}
)[
petab_problem_measurements.measurement_df[
petab.v1.C.OBSERVABLE_ID
].str.startswith("prior_")
],
simulation_df_priors,
# for the prior observables, we need to "simulate" the model with the
# nominal parameter values
simulated_prior_observables = (
petab_problem_measurements.measurement_df.rename(
columns={MEASUREMENT: SIMULATION}
)[
petab_problem_measurements.measurement_df[
OBSERVABLE_ID
].str.startswith("prior_")
]
)

def apply_parameter_values(row):
# apply the parameter values to the observable formula for the prior
if row[OBSERVABLE_ID].startswith("prior_"):
row[SIMULATION] = x_scaled_dict[
row[OBSERVABLE_ID].removeprefix("prior_")
]
return row

simulated_prior_observables = simulated_prior_observables.apply(
apply_parameter_values, axis=1
)
simulation_df_measurements = pd.concat(
[simulation_df_priors, simulated_prior_observables]
)

llh_priors = petab.v1.calculate_llh_for_table(
petab_problem_priors.measurement_df,
simulation_df_priors,
Expand All @@ -102,10 +137,8 @@ def test_priors_to_measurements(problem_id):

# get prior objective function contribution
parameter_ids = petab_problem_priors.parameter_df.index.values[
(petab_problem_priors.parameter_df[petab.v1.ESTIMATE] == 1)
& petab_problem_priors.parameter_df[
petab.v1.OBJECTIVE_PRIOR_TYPE
].notna()
(petab_problem_priors.parameter_df[ESTIMATE] == 1)
& petab_problem_priors.parameter_df[OBJECTIVE_PRIOR_TYPE].notna()
]
priors = petab.v1.get_priors_from_df(
petab_problem_priors.parameter_df,
Expand All @@ -117,9 +150,7 @@ def test_priors_to_measurements(problem_id):
prior_type, prior_pars, par_scale, par_bounds = prior
if prior_type == petab.v1.PARAMETER_SCALE_NORMAL:
prior_contrib += norm.logpdf(
petab_problem_priors.x_nominal_free_scaled[
petab_problem_priors.x_free_ids.index(parameter_id)
],
x_scaled_dict[parameter_id],
loc=prior_pars[0],
scale=prior_pars[1],
)
Expand All @@ -134,4 +165,4 @@ def test_priors_to_measurements(problem_id):
llh_priors + prior_contrib, llh_measurements, rtol=1e-3, atol=1e-16
), (llh_priors + prior_contrib, llh_measurements)
# check that the tolerance is not too high
assert np.abs(prior_contrib) > 1e-3 * np.abs(llh_priors)
assert np.abs(prior_contrib) > 1e-8 * np.abs(llh_priors)
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