Allow plot_expected_purchases_pcc in BetaGeoModel and ModifiedBetaGeoModel

environment.yml

@@ -14,6 +14,7 @@ dependencies:
 - pip
 - pydantic
 - preliz
+- pyprojroot
 # NOTE: Keep minimum pymc version in sync with ci.yml `OLDEST_PYMC_VERSION`
 - pymc>=5.20.0
 - pymc-extras>=0.2.1

pymc_marketing/clv/models/beta_geo.py

@@ -57,8 +57,8 @@ class BetaGeoModel(CLVModel):
             * `T`: Time between the first purchase and the end of the observation period
     model_config : dict, optional
         Dictionary of model prior parameters:
-            * `alpha_prior`: Scale parameter for time between purchases; defaults to `Prior("HalfFlat")`
-            * `r_prior`: Shape parameter for time between purchases; defaults to `Prior("HalfFlat")`
+            * `alpha_prior`: Scale parameter for time between purchases; defaults to `Prior("Weibull", alpha=2, beta=10)`
+            * `r_prior`: Shape parameter for time between purchases; defaults to `Prior("Weibull", alpha=2, beta=1)`
             * `a_prior`: Shape parameter of dropout process; defaults to `phi_purchase_prior` * `kappa_purchase_prior`
             * `b_prior`: Shape parameter of dropout process; defaults to `1-phi_dropout_prior` * `kappa_dropout_prior`
             * `phi_dropout_prior`: Nested prior for a and b priors; defaults to `Prior("Uniform", lower=0, upper=1)`
@@ -96,10 +96,10 @@ class BetaGeoModel(CLVModel):
         model = BetaGeoModel(
             data=data,
             model_config={
-                "r_prior": Prior("HalfFlat"),
+                "r_prior": Prior("Weibull", alpha=2, beta=1),
                 "alpha_prior": Prior("HalfFlat"),
-                "a_prior": Prior("HalfFlat"),
-                "b_prior": Prior("HalfFlat),
+                "a_prior": Prior("Beta", alpha=2, beta=3),
+                "b_prior": Prior("Beta", alpha=3, beta=2),
             },
             sampler_config={
                 "draws": 1000,
@@ -166,8 +166,8 @@ def __init__(
     def default_model_config(self) -> ModelConfig:
         """Default model configuration."""
         return {
-            "alpha_prior": Prior("HalfFlat"),
-            "r_prior": Prior("HalfFlat"),
+            "alpha_prior": Prior("Weibull", alpha=2, beta=10),
+            "r_prior": Prior("Weibull", alpha=2, beta=1),
             "phi_dropout_prior": Prior("Uniform", lower=0, upper=1),
             "kappa_dropout_prior": Prior("Pareto", alpha=1, m=1),
         }

pymc_marketing/clv/plotting.py

@@ -488,7 +488,7 @@ def plot_expected_purchases_ppc(
 ) -> plt.Axes:
     """Plot a prior or posterior predictive check for the customer purchase frequency distribution.
 
-    At this time only ``ParetoNBDModel`` and ``BetaGeoBetaBinomModel`` are supported.
+    ``ParetoNBDModel``, ``BetaGeoBetaBinomModel``, ``BetaGeoModel`` and ``ModifiedBetaGeoModel`` are supported.
 
     Adapted from legacy ``lifetimes`` library:
     https://github.com/CamDavidsonPilon/lifetimes/blob/master/lifetimes/plotting.py#L25
@@ -515,10 +515,6 @@ def plot_expected_purchases_ppc(
     -------
     axes : matplotlib.AxesSubplot
     """
-    # TODO: BetaGeoModel requires its own dist class in distributions.py for this function.
-    if isinstance(model, BetaGeoModel):
-        raise AttributeError("BetaGeoModel is unsupported for this function.")
-
     if ax is None:
         ax = plt.subplot(111)
 

tests/clv/test_plotting.py

@@ -182,25 +182,22 @@ def test_plot_expected_purchases_over_time(
     plt.clf()
 
 
-def test_plot_expected_purchases_ppc_exceptions(fitted_bg, fitted_pnbd):
-    with pytest.raises(
-        AttributeError, match="BetaGeoModel is unsupported for this function."
-    ):
-        plot_expected_purchases_ppc(fitted_bg)
-
+def test_plot_expected_purchases_ppc_exceptions(fitted_model):
     with pytest.raises(
         NameError, match="Specify 'prior' or 'posterior' for 'ppc' parameter."
     ):
-        plot_expected_purchases_ppc(fitted_pnbd, ppc="ppc")
+        plot_expected_purchases_ppc(fitted_model, ppc="ppc")
 
 
 @pytest.mark.parametrize(
     "ppc, max_purchases, samples, subplot",
     [("prior", 10, 100, None), ("posterior", 20, 50, plt.subplot())],
 )
-def test_plot_expected_purchases_ppc(fitted_pnbd, ppc, max_purchases, samples, subplot):
+def test_plot_expected_purchases_ppc(
+    fitted_model, ppc, max_purchases, samples, subplot
+):
     ax = plot_expected_purchases_ppc(
-        model=fitted_pnbd,
+        model=fitted_model,
         ppc=ppc,
         max_purchases=max_purchases,
         samples=samples,

tests/conftest.py

@@ -21,7 +21,12 @@
 from arviz import InferenceData
 from xarray import DataArray, Dataset
 
-from pymc_marketing.clv.models import BetaGeoModel, CLVModel, ParetoNBDModel
+from pymc_marketing.clv.models import (
+    BetaGeoModel,
+    CLVModel,
+    ModifiedBetaGeoModel,
+    ParetoNBDModel,
+)
 from pymc_marketing.prior import Prior
 
 
@@ -191,6 +196,32 @@ def fitted_bg(test_summary_data) -> BetaGeoModel:
     return model
 
 
+# TODO: This fixture is used in the plotting and utils test modules.
+#       Consider creating a MockModel class to replace this and other fitted model fixtures.
+@pytest.fixture(scope="module")
+def fitted_mbg(test_summary_data) -> ModifiedBetaGeoModel:
+    rng = np.random.default_rng(13)
+
+    model_config = {
+        # Narrow Gaussian centered at MLE params from lifetimes BetaGeoFitter
+        "a_prior": Prior("DiracDelta", c=1.85034151),
+        "alpha_prior": Prior("DiracDelta", c=1.86428187),
+        "b_prior": Prior("DiracDelta", c=3.18105431),
+        "r_prior": Prior("DiracDelta", c=0.16385072),
+    }
+    model = ModifiedBetaGeoModel(
+        data=test_summary_data,
+        model_config=model_config,
+    )
+    model.build_model()
+    fake_fit = pm.sample_prior_predictive(draws=50, model=model.model, random_seed=rng)
+    # posterior group required to pass L80 assert check
+    fake_fit.add_groups(posterior=fake_fit.prior)
+    set_model_fit(model, fake_fit)
+
+    return model
+
+
 # TODO: This fixture is used in the plotting and utils test modules.
 #       Consider creating a MockModel class to replace this and other fitted model fixtures.
 @pytest.fixture(scope="module")
@@ -222,3 +253,13 @@ def fitted_pnbd(test_summary_data) -> ParetoNBDModel:
     set_model_fit(pnbd_model, fake_fit)
 
     return pnbd_model
+
+
+@pytest.fixture(params=["bg_model", "mbg_model", "pnbd_model"])
+def fitted_model(request, fitted_bg, fitted_mbg, fitted_pnbd):
+    fitted_models = {
+        "bg_model": fitted_bg,
+        "mbg_model": fitted_mbg,
+        "pnbd_model": fitted_pnbd,
+    }
+    return fitted_models[request.param]