Out-of-Box MultiDimensional MMM

docs/source/notebooks/mmm/mmm_components.ipynb

@@ -49,7 +49,7 @@
     "import xarray as xr\n",
     "\n",
     "from pymc_marketing import mmm\n",
-    "from pymc_marketing.mmm.plot import plot_curve\n",
+    "from pymc_marketing.plot import plot_curve\n",
     "from pymc_marketing.prior import Prior\n",
     "\n",
     "az.style.use(\"arviz-darkgrid\")\n",

pymc_marketing/mmm/budget_optimizer.py

@@ -167,10 +167,10 @@ def __init__(self, **data):
         # 1. Prepare model with time dimension for optimization
         pymc_model = self.mmm_model._set_predictors_for_optimization(self.num_periods)
 
-        # 2. Shared variable for total_budget
+        # 2. Shared variable for total_budget: Use annotation to avoid type checking
         self._total_budget: SharedVariable = shared(
             np.array(0.0, dtype="float64"), name="total_budget"
-        )
+        )  # type: ignore
 
         # 3. Identify budget dimensions and shapes
         self._budget_dims = [

pymc_marketing/mmm/hsgp.py

@@ -679,7 +679,7 @@ def parameterize_from_data(
             eta=eta,
             m=m,
             L=L,
-            X=X,
+            X=X,  # store the original reference (even if symbolic)
             X_mid=X_mid,
             cov_func=cov_func,
             dims=dims,