Add BetaGeoBetaBinomModel (#1031)

* beta_geo_beta_binom.py copy from prev pr

* test_beta_geo_beta_binom.py copy from prev PR

* beta_geo_beta_binom imports

* basic.py validate homogeneous T

* copy _logp fix from prev PR

* notebook and WIP _distribution_new_customers

* test_distribution_new_customers

* TODOs and test coverage

* docstrings

* docstrings

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

pymc_marketing/clv/__init__.py

@@ -14,6 +14,7 @@
 """CLV models and utilities."""
 
 from pymc_marketing.clv.models import (
+    BetaGeoBetaBinomModel,
     BetaGeoModel,
     GammaGammaModel,
     GammaGammaModelIndividual,
@@ -34,6 +35,7 @@
 
 __all__ = (
     "BetaGeoModel",
+    "BetaGeoBetaBinomModel",
     "ParetoNBDModel",
     "GammaGammaModel",
     "GammaGammaModelIndividual",

pymc_marketing/clv/distributions.py

@@ -601,23 +601,17 @@ def logp(value, alpha, beta, gamma, delta, T):
         """Log-likelihood of the distribution."""
         t_x = pt.atleast_1d(value[..., 0])
         x = pt.atleast_1d(value[..., 1])
-        scalar_case = t_x.type.broadcastable == (True,)
 
         for param in (t_x, x, alpha, beta, gamma, delta, T):
             if param.type.ndim > 1:
                 raise NotImplementedError(
                     f"BetaGeoBetaBinom logp only implemented for vector parameters, got ndim={param.type.ndim}"
                 )
-            if scalar_case:
-                if param.type.broadcastable == (False,):
-                    raise NotImplementedError(
-                        f"Parameter {param} cannot be larger than scalar value"
-                    )
 
         # Broadcast all the parameters so they are sequences.
         # Potentially inefficient, but otherwise ugly logic needed to unpack arguments in the scan function,
         # since sequences always precede non-sequences.
-        _, alpha, beta, gamma, delta, T = pt.broadcast_arrays(
+        t_x, alpha, beta, gamma, delta, T = pt.broadcast_arrays(
             t_x, alpha, beta, gamma, delta, T
         )
 

pymc_marketing/clv/models/__init__.py

@@ -16,6 +16,7 @@
 
 from pymc_marketing.clv.models.basic import CLVModel
 from pymc_marketing.clv.models.beta_geo import BetaGeoModel
+from pymc_marketing.clv.models.beta_geo_beta_binom import BetaGeoBetaBinomModel
 from pymc_marketing.clv.models.gamma_gamma import (
     GammaGammaModel,
     GammaGammaModelIndividual,
@@ -25,6 +26,7 @@
 
 __all__ = (
     "CLVModel",
+    "BetaGeoBetaBinomModel",
     "GammaGammaModel",
     "GammaGammaModelIndividual",
     "BetaGeoModel",

pymc_marketing/clv/models/basic.py

@@ -61,6 +61,7 @@ def _validate_cols(
         data: pd.DataFrame,
         required_cols: Sequence[str],
         must_be_unique: Sequence[str] = (),
+        must_be_homogenous: Sequence[str] = (),
     ):
         existing_columns = set(data.columns)
         n = data.shape[0]
@@ -71,6 +72,11 @@ def _validate_cols(
             if required_col in must_be_unique:
                 if data[required_col].nunique() != n:
                     raise ValueError(f"Column {required_col} has duplicate entries")
+            if required_col in must_be_homogenous:
+                if data[required_col].nunique() != 1:
+                    raise ValueError(
+                        f"Column {required_col} has  non-homogeneous entries"
+                    )
 
     def __repr__(self) -> str:
         """Representation of the model."""