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Validator: check for positive bounds for log-scaled parameter #278

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merged 7 commits into from
Jun 27, 2024
Merged

Validator: check for positive bounds for log-scaled parameter #278

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0c5cd18
Validator: check for positive bounds for log-scaled parameter
dweindl Jun 26, 2024
116bf8c
Merge branch 'develop' into lint_pos_log
dweindl Jun 26, 2024
47582ac
Merge branch 'develop' into lint_pos_log
dweindl Jun 26, 2024
00bb950
Merge branch 'develop' into lint_pos_log
dweindl Jun 26, 2024
75fc0ab
priors
dweindl Jun 26, 2024
995b262
Merge branch 'develop' into lint_pos_log
dweindl Jun 26, 2024
0fce8b2
only init prior
dweindl Jun 27, 2024
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24 changes: 24 additions & 0 deletions petab/lint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -558,6 +558,17 @@ def check_parameter_bounds(parameter_df: pd.DataFrame) -> None:
f"Bounds for {row[PARAMETER_SCALE]} scaled parameter "
f"{ row.name} must be positive."
)
if (
row.get(PARAMETER_SCALE, LIN) in [LOG, LOG10]
and (row[LOWER_BOUND] == 0.0 or row[UPPER_BOUND] == 0.0)
and not row.get(INITIALIZATION_PRIOR_TYPE)
):
Comment on lines +561 to +565
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I would move row[UPPER_BOUND] == 0.0 above, i.e. change row[UPPER_BOUND] < 0.0 to row[UPPER_BOUND] <= 0.0 -- does not make sense to estimate something that can only take the value 0.0.

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lb=ub is allowed so far. I'd say things that are legal but dumb shouldn't raise exceptions. Although we might want to add some optional hints for improvement at some point.

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Fine for me 👍 Should we check the initialization prior then, to make sure it's sampling in finite bounds?

raise AssertionError(
f"Bounds for {row[PARAMETER_SCALE]} scaled parameter "
f"{row.name} must be positive if no "
f"{INITIALIZATION_PRIOR_TYPE} is provided. "
"Cannot sample from unbounded interval."
)


def assert_parameter_prior_type_is_valid(parameter_df: pd.DataFrame) -> None:
Expand Down Expand Up @@ -636,6 +647,19 @@ def assert_parameter_prior_parameters_are_valid(
f"{PARAMETER_SEPARATOR}par2' for all prior types)."
)

# we can't sample uniformly from [log(0)=-inf, ...]
if (
type_col == INITIALIZATION_PRIOR_TYPE
and row.get(type_col, "") == PARAMETER_SCALE_UNIFORM
and row.get(PARAMETER_SCALE, LIN) in [LOG, LOG10]
and (pars[0] == 0.0 or pars[1] == 0.0)
):
raise AssertionError(
f"{prior_par_cols} for {row[PARAMETER_SCALE]} scaled "
f"parameter {row.name} must be positive if "
f"{type_col}={PARAMETER_SCALE_UNIFORM}."
)


def assert_parameter_estimate_is_boolean(parameter_df: pd.DataFrame) -> None:
"""
Expand Down
15 changes: 15 additions & 0 deletions tests/test_lint.py
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Expand Up @@ -642,3 +642,18 @@ def test_parameter_ids_are_unique():
parameter_df.index = ["par0", "par1"]
parameter_df.index.name = "parameterId"
lint.check_parameter_df(parameter_df)


def test_check_positive_bounds_for_scaled_parameters():
parameter_df = pd.DataFrame(
{
PARAMETER_ID: ["par"],
PARAMETER_SCALE: [LOG10],
ESTIMATE: [1],
LOWER_BOUND: [0.0],
UPPER_BOUND: [1],
}
).set_index(PARAMETER_ID)

with pytest.raises(AssertionError, match="positive"):
lint.check_parameter_df(parameter_df)