Skip to content

refactor(clv): drop _prior suffix backwards-compat shim #2576

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Draft
anevolbap wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

refactor(clv): drop _prior suffix backwards-compat shim #2576

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
66ee144
refactor(clv): drop _prior suffix backwards-compat shim
anevolbap May 17, 2026
c5fc5b2
Merge branch 'main' into refactor/1516-clv-prior-suffix-shim
anevolbap May 18, 2026
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions docs/source/notebooks/clv/dev/bg_nbg_covariates_test_issues.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -318,8 +318,8 @@
"dropout_covariate_cols = [\"dropout_cov\"]\n",
"\n",
"non_nested_priors = dict(\n",
" a_prior=Prior(\"Uniform\", lower=0, upper=1),\n",
" b_prior=Prior(\"Uniform\", lower=0, upper=1),\n",
" a=Prior(\"Uniform\", lower=0, upper=1),\n",
" b=Prior(\"Uniform\", lower=0, upper=1),\n",
")\n",
"covariate_config = dict(\n",
" purchase_covariate_cols=purchase_covariate_cols,\n",
Expand Down
27 changes: 0 additions & 27 deletions pymc_marketing/clv/models/basic.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -54,18 +54,6 @@ def __init__(

model_config = model_config or {}

deprecated_keys = [key for key in model_config if key.endswith("_prior")]
for key in deprecated_keys:
new_key = key.replace("_prior", "")
warnings.warn(
f"The key '{key}' in model_config is deprecated and will be removed in future versions."
f"Use '{new_key}' instead.",
DeprecationWarning,
stacklevel=2,
)

model_config[new_key] = model_config.pop(key)

super().__init__(model_config, sampler_config)

# Parse model config after merging with defaults
Expand Down Expand Up @@ -310,7 +298,6 @@ def build_from_idata(cls, idata: az.InferenceData) -> None:
model = cls(**kwargs)

model.idata = idata
model._rename_posterior_variables()
model.data = idata.fit_data.to_dataframe()

model.build_model(model.data) # type: ignore
Expand All @@ -324,20 +311,6 @@ def build_from_idata(cls, idata: az.InferenceData) -> None:
raise DifferentModelError(msg)
return model

# TODO: Remove for v1.0
def _rename_posterior_variables(self):
"""Rename variables in the posterior group to remove the _prior suffix.

This is used to support the old model configuration format, which used
to include a _prior suffix for each parameter.
"""
prior_vars = [
var for var in self.idata.posterior.data_vars if var.endswith("_prior")
]
rename_dict = {var: var.replace("_prior", "") for var in prior_vars}
self.idata.posterior = self.idata.posterior.rename(rename_dict)
return self.idata.posterior

def thin_fit_result(self, keep_every: int):
"""Return a copy of the model with a thinned fit result.

Expand Down
29 changes: 0 additions & 29 deletions tests/clv/models/test_basic.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -309,35 +309,6 @@ def test_model_config_warns(self) -> None:
"x": Prior("StudentT", mu=0, sigma=5, nu=15),
}

def test_backwards_compatibility_with_old_config(self):
model = CLVModelTest()
model.build_model()

old_posterior = from_dict(posterior={"alpha_prior": np.random.randn(2, 100)})
set_model_fit(model, old_posterior)
assert "alpha_prior" in model.idata.posterior

save_path = "test_model"
model.save(save_path)

loaded_model = CLVModelTest.load(save_path)

assert "alpha" in loaded_model.idata.posterior
assert "alpha_prior" not in loaded_model.idata.posterior

os.remove("test_model")

def test_deprecation_warning_on_old_config(self):
old_model_config = {
"x_prior": {"dist": "Normal", "kwargs": {"mu": 0, "sigma": 1}}
}
with pytest.warns(
DeprecationWarning, match=r"The key 'x_prior' in model_config is deprecated"
):
model = CLVModelTest(model_config=old_model_config)

assert model.model_config == {"x": Prior("Normal", mu=0, sigma=1)}

def test_validate_cols_reports_all_missing_columns(self):
"""Test _validate_cols raises a single ValueError listing all missing columns."""
required = ("customer_id", "frequency", "recency", "T")
Expand Down
28 changes: 14 additions & 14 deletions tests/clv/models/test_beta_geo.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -712,8 +712,8 @@ def setup_class(cls):
purchase_covariate_cols = ["purchase_cov1", "purchase_cov2"]
dropout_covariate_cols = ["dropout_cov"]
non_nested_priors = dict(
a_prior=Prior("Beta", alpha=20, beta=20),
b_prior=Prior("Beta", alpha=20, beta=20),
a=Prior("Beta", alpha=20, beta=20),
b=Prior("Beta", alpha=20, beta=20),
)
covariate_config = dict(
purchase_covariate_cols=purchase_covariate_cols,
Expand Down Expand Up @@ -1004,12 +1004,12 @@ def test_covariate_model_convergence_a_b(self):
)
# The default parameter priors are very informative. We use something broader here
custom_priors = {
"r_prior": Prior("Exponential", scale=10),
"alpha_prior": Prior("Exponential", scale=10),
"a_prior": Prior("Exponential", scale=10),
"b_prior": Prior("Exponential", scale=10),
"purchase_coefficient_prior": Prior("Normal", mu=0, sigma=4),
"dropout_coefficient_prior": Prior("Normal", mu=0, sigma=4),
"r": Prior("Exponential", scale=10),
"alpha": Prior("Exponential", scale=10),
"a": Prior("Exponential", scale=10),
"b": Prior("Exponential", scale=10),
"purchase_coefficient": Prior("Normal", mu=0, sigma=4),
"dropout_coefficient": Prior("Normal", mu=0, sigma=4),
}
new_model = BetaGeoModel(
synthetic_data,
Expand Down Expand Up @@ -1046,12 +1046,12 @@ def test_covariate_model_convergence_phi_kappa(self):
)
# The default parameter priors are very informative. We use something broader here
custom_priors = {
"r_prior": Prior("Exponential", scale=10),
"alpha_prior": Prior("Exponential", scale=10),
"phi_dropout_prior": Prior("Uniform", lower=0, upper=1),
"kappa_dropout_prior": Prior("Pareto", alpha=1, m=1),
"purchase_coefficient_prior": Prior("Normal", mu=0, sigma=5),
"dropout_coefficient_prior": Prior("Normal", mu=0, sigma=5),
"r": Prior("Exponential", scale=10),
"alpha": Prior("Exponential", scale=10),
"phi_dropout": Prior("Uniform", lower=0, upper=1),
"kappa_dropout": Prior("Pareto", alpha=1, m=1),
"purchase_coefficient": Prior("Normal", mu=0, sigma=5),
"dropout_coefficient": Prior("Normal", mu=0, sigma=5),
}
new_model = BetaGeoModel(
synthetic_data,
Expand Down
28 changes: 14 additions & 14 deletions tests/clv/models/test_modified_beta_geo.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -516,8 +516,8 @@ def setup_class(cls):
purchase_covariate_cols = ["purchase_cov1", "purchase_cov2"]
dropout_covariate_cols = ["dropout_cov"]
non_nested_priors = dict(
a_prior=Prior("Beta", alpha=20, beta=20),
b_prior=Prior("Beta", alpha=20, beta=20),
a=Prior("Beta", alpha=20, beta=20),
b=Prior("Beta", alpha=20, beta=20),
)
covariate_config = dict(
purchase_covariate_cols=purchase_covariate_cols,
Expand Down Expand Up @@ -808,12 +808,12 @@ def test_covariate_model_convergence_a_b(self):
)
# The default parameter priors are very informative. We use something broader here
custom_priors = {
"r_prior": Prior("HalfFlat"),
"alpha_prior": Prior("HalfFlat"),
"a_prior": Prior("HalfFlat"),
"b_prior": Prior("HalfFlat"),
"purchase_coefficient_prior": Prior("Normal", mu=0, sigma=4),
"dropout_coefficient_prior": Prior("Normal", mu=0, sigma=4),
"r": Prior("HalfFlat"),
"alpha": Prior("HalfFlat"),
"a": Prior("HalfFlat"),
"b": Prior("HalfFlat"),
"purchase_coefficient": Prior("Normal", mu=0, sigma=4),
"dropout_coefficient": Prior("Normal", mu=0, sigma=4),
}
new_model = ModifiedBetaGeoModel(
model_config=self.model_with_covariates.model_config | custom_priors,
Expand Down Expand Up @@ -849,12 +849,12 @@ def test_covariate_model_convergence_phi_kappa(self):
)
# The default parameter priors are very informative. We use something broader here
custom_priors = {
"r_prior": Prior("HalfFlat"),
"alpha_prior": Prior("HalfFlat"),
"phi_dropout_prior": Prior("Uniform", lower=0, upper=1),
"kappa_dropout_prior": Prior("Pareto", alpha=1, m=1),
"purchase_coefficient_prior": Prior("Flat"),
"dropout_coefficient_prior": Prior("Flat"),
"r": Prior("HalfFlat"),
"alpha": Prior("HalfFlat"),
"phi_dropout": Prior("Uniform", lower=0, upper=1),
"kappa_dropout": Prior("Pareto", alpha=1, m=1),
"purchase_coefficient": Prior("Flat"),
"dropout_coefficient": Prior("Flat"),
}
new_model = ModifiedBetaGeoModel(
synthetic_data,
Expand Down