Merge branch 'main' into mmm_casestudy

docs/source/notebooks/mmm/mmm_components.ipynb

@@ -135,7 +135,7 @@
     "\n",
     "alpha = 1\n",
     "lam = 1 / 10\n",
-    "yy = saturation.function(xx, alpha=alpha, lam=lam)\n",
+    "yy = saturation.function(xx, alpha=alpha, lam=lam).eval()\n",
     "\n",
     "fig, ax = plt.subplots()\n",
     "fig.suptitle(\"Example Saturation Curve\")\n",

pymc_marketing/mmm/components/saturation.py

@@ -72,6 +72,7 @@ def function(self, x, b):
 """
 
 import numpy as np
+import pytensor.tensor as pt
 import xarray as xr
 from pydantic import Field, InstanceOf, validate_call
 
@@ -337,7 +338,9 @@ class MichaelisMentenSaturation(SaturationTransformation):
 
     lookup_name = "michaelis_menten"
 
-    function = michaelis_menten
+    def function(self, x, alpha, lam):
+        """Michaelis-Menten saturation function."""
+        return pt.as_tensor_variable(michaelis_menten(x, alpha, lam))
 
     default_priors = {
         "alpha": Prior("Gamma", mu=2, sigma=1),

tests/mmm/test_plotting.py

@@ -20,7 +20,10 @@
 from matplotlib import pyplot as plt
 
 from pymc_marketing.mmm.components.adstock import GeometricAdstock
-from pymc_marketing.mmm.components.saturation import LogisticSaturation
+from pymc_marketing.mmm.components.saturation import (
+    LogisticSaturation,
+    MichaelisMentenSaturation,
+)
 from pymc_marketing.mmm.mmm import MMM, BaseMMM
 from pymc_marketing.mmm.preprocessing import MaxAbsScaleTarget
 
@@ -220,10 +223,18 @@ def test_plots(self, plotting_mmm, func_plot_name, kwargs_plot) -> None:
         plt.close("all")
 
 
+@pytest.fixture(
+    scope="module",
+    params=[LogisticSaturation(), MichaelisMentenSaturation()],
+    ids=["LogisticSaturation", "MichaelisMentenSaturation"],
+)
+def saturation(request):
+    return request.param
+
+
 @pytest.fixture(scope="module")
-def mock_mmm() -> MMM:
+def mock_mmm(saturation) -> MMM:
     adstock = GeometricAdstock(l_max=4)
-    saturation = LogisticSaturation()
     return MMM(
         date_column="date",
         channel_columns=["channel_1", "channel_2"],