Add prior predictive example notebook (#787)

pymc-labs · Sep 10, 2024 · 7f469c4 · 7f469c4
1 parent 53e2f6d
commit 7f469c4
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Showing 4 changed files with 4,365 additions and 3,693 deletions.
diff --git a/docs/source/notebooks/mmm/mmm_example.ipynb b/docs/source/notebooks/mmm/mmm_example.ipynb
diff --git a/pymc_marketing/mmm/base.py b/pymc_marketing/mmm/base.py
@@ -293,9 +293,7 @@ def posterior_predictive(self) -> Dataset:
             )
         return self.idata["posterior_predictive"]
 
-    def plot_prior_predictive(
-        self, samples: int = 1_000, **plt_kwargs: Any
-    ) -> plt.Figure:
+    def plot_prior_predictive(self, **plt_kwargs: Any) -> plt.Figure:
         prior_predictive_data: az.InferenceData = self.prior_predictive
 
         likelihood_hdi_94: DataArray = az.hdi(ary=prior_predictive_data, hdi_prob=0.94)[

diff --git a/pymc_marketing/model_builder.py b/pymc_marketing/model_builder.py
@@ -578,7 +578,6 @@ def sample_prior_predictive(
         if not hasattr(self, "model"):
             self.build_model(X_pred, y_pred)
 
-        self._data_setter(X_pred, y_pred)
         with self.model:  # sample with new input data
             prior_pred: az.InferenceData = pm.sample_prior_predictive(samples, **kwargs)
             self.set_idata_attrs(prior_pred)

diff --git a/tests/mmm/test_plotting.py b/tests/mmm/test_plotting.py
@@ -17,10 +17,7 @@
 import pytest
 from matplotlib import pyplot as plt
 
-from pymc_marketing.mmm.delayed_saturated_mmm import (
-    MMM,
-    BaseMMM,
-)
+from pymc_marketing.mmm.delayed_saturated_mmm import MMM, BaseMMM
 from pymc_marketing.mmm.preprocessing import MaxAbsScaleTarget
 
 seed: int = sum(map(ord, "pymc_marketing"))
@@ -124,7 +121,7 @@ class ToyMMM(BaseMMM, MaxAbsScaleTarget):
     @pytest.mark.parametrize(
         argnames="func_plot_name, kwargs_plot",
         argvalues=[
-            ("plot_prior_predictive", {"samples": 3}),
+            ("plot_prior_predictive", {}),
             ("plot_posterior_predictive", {}),
             ("plot_posterior_predictive", {"original_scale": True}),
             ("plot_posterior_predictive", {"ax": plt.subplots()[1]}),